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Priya ranganathan.
1 Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India
2 Department of Surgical Oncology, Tata Memorial Centre, Mumbai, Maharashtra, India
The second article in this series on biostatistics covers the concepts of sample, population, research hypotheses and statistical errors.
Ranganathan P, Pramesh CS. An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors. Indian J Crit Care Med 2019;23(Suppl 3):S230–S231.
Two papers quoted in this issue of the Indian Journal of Critical Care Medicine report. The results of studies aim to prove that a new intervention is better than (superior to) an existing treatment. In the ABLE study, the investigators wanted to show that transfusion of fresh red blood cells would be superior to standard-issue red cells in reducing 90-day mortality in ICU patients. 1 The PROPPR study was designed to prove that transfusion of a lower ratio of plasma and platelets to red cells would be superior to a higher ratio in decreasing 24-hour and 30-day mortality in critically ill patients. 2 These studies are known as superiority studies (as opposed to noninferiority or equivalence studies which will be discussed in a subsequent article).
A sample represents a group of participants selected from the entire population. Since studies cannot be carried out on entire populations, researchers choose samples, which are representative of the population. This is similar to walking into a grocery store and examining a few grains of rice or wheat before purchasing an entire bag; we assume that the few grains that we select (the sample) are representative of the entire sack of grains (the population).
The results of the study are then extrapolated to generate inferences about the population. We do this using a process known as hypothesis testing. This means that the results of the study may not always be identical to the results we would expect to find in the population; i.e., there is the possibility that the study results may be erroneous.
A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the “alternate” hypothesis, and the opposite is called the “null” hypothesis; every study has a null hypothesis and an alternate hypothesis. For superiority studies, the alternate hypothesis states that one treatment (usually the new or experimental treatment) is superior to the other; the null hypothesis states that there is no difference between the treatments (the treatments are equal). For example, in the ABLE study, we start by stating the null hypothesis—there is no difference in mortality between groups receiving fresh RBCs and standard-issue RBCs. We then state the alternate hypothesis—There is a difference between groups receiving fresh RBCs and standard-issue RBCs. It is important to note that we have stated that the groups are different, without specifying which group will be better than the other. This is known as a two-tailed hypothesis and it allows us to test for superiority on either side (using a two-sided test). This is because, when we start a study, we are not 100% certain that the new treatment can only be better than the standard treatment—it could be worse, and if it is so, the study should pick it up as well. One tailed hypothesis and one-sided statistical testing is done for non-inferiority studies, which will be discussed in a subsequent paper in this series.
There are two possibilities to consider when interpreting the results of a superiority study. The first possibility is that there is truly no difference between the treatments but the study finds that they are different. This is called a Type-1 error or false-positive error or alpha error. This means falsely rejecting the null hypothesis.
The second possibility is that there is a difference between the treatments and the study does not pick up this difference. This is called a Type 2 error or false-negative error or beta error. This means falsely accepting the null hypothesis.
The power of the study is the ability to detect a difference between groups and is the converse of the beta error; i.e., power = 1-beta error. Alpha and beta errors are finalized when the protocol is written and form the basis for sample size calculation for the study. In an ideal world, we would not like any error in the results of our study; however, we would need to do the study in the entire population (infinite sample size) to be able to get a 0% alpha and beta error. These two errors enable us to do studies with realistic sample sizes, with the compromise that there is a small possibility that the results may not always reflect the truth. The basis for this will be discussed in a subsequent paper in this series dealing with sample size calculation.
Conventionally, type 1 or alpha error is set at 5%. This means, that at the end of the study, if there is a difference between groups, we want to be 95% certain that this is a true difference and allow only a 5% probability that this difference has occurred by chance (false positive). Type 2 or beta error is usually set between 10% and 20%; therefore, the power of the study is 90% or 80%. This means that if there is a difference between groups, we want to be 80% (or 90%) certain that the study will detect that difference. For example, in the ABLE study, sample size was calculated with a type 1 error of 5% (two-sided) and power of 90% (type 2 error of 10%) (1).
Table 1 gives a summary of the two types of statistical errors with an example
Statistical errors
(a) Types of statistical errors | |||
: Null hypothesis is | |||
True | False | ||
Null hypothesis is actually | True | Correct results! | Falsely rejecting null hypothesis - Type I error |
False | Falsely accepting null hypothesis - Type II error | Correct results! | |
(b) Possible statistical errors in the ABLE trial | |||
There is difference in mortality between groups receiving fresh RBCs and standard-issue RBCs | There difference in mortality between groups receiving fresh RBCs and standard-issue RBCs | ||
Truth | There is difference in mortality between groups receiving fresh RBCs and standard-issue RBCs | Correct results! | Falsely rejecting null hypothesis - Type I error |
There difference in mortality between groups receiving fresh RBCs and standard-issue RBCs | Falsely accepting null hypothesis - Type II error | Correct results! |
In the next article in this series, we will look at the meaning and interpretation of ‘ p ’ value and confidence intervals for hypothesis testing.
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hypothesis , something supposed or taken for granted, with the object of following out its consequences (Greek hypothesis , “a putting under,” the Latin equivalent being suppositio ).
In planning a course of action, one may consider various alternatives , working out each in detail. Although the word hypothesis is not typically used in this case, the procedure is virtually the same as that of an investigator of crime considering various suspects. Different methods may be used for deciding what the various alternatives may be, but what is fundamental is the consideration of a supposal as if it were true, without actually accepting it as true. One of the earliest uses of the word in this sense was in geometry . It is described by Plato in the Meno .
The most important modern use of a hypothesis is in relation to scientific investigation . A scientist is not merely concerned to accumulate such facts as can be discovered by observation: linkages must be discovered to connect those facts. An initial puzzle or problem provides the impetus , but clues must be used to ascertain which facts will help yield a solution. The best guide is a tentative hypothesis, which fits within the existing body of doctrine. It is so framed that, with its help, deductions can be made that under certain factual conditions (“initial conditions”) certain other facts would be found if the hypothesis were correct.
The concepts involved in the hypothesis need not themselves refer to observable objects. However, the initial conditions should be able to be observed or to be produced experimentally, and the deduced facts should be able to be observed. William Harvey ’s research on circulation in animals demonstrates how greatly experimental observation can be helped by a fruitful hypothesis. While a hypothesis can be partially confirmed by showing that what is deduced from it with certain initial conditions is actually found under those conditions, it cannot be completely proved in this way. What would have to be shown is that no other hypothesis would serve. Hence, in assessing the soundness of a hypothesis, stress is laid on the range and variety of facts that can be brought under its scope. Again, it is important that it should be capable of being linked systematically with hypotheses which have been found fertile in other fields.
If the predictions derived from the hypothesis are not found to be true, the hypothesis may have to be given up or modified. The fault may lie, however, in some other principle forming part of the body of accepted doctrine which has been utilized in deducing consequences from the hypothesis. It may also lie in the fact that other conditions, hitherto unobserved, are present beside the initial conditions, affecting the result. Thus the hypothesis may be kept, pending further examination of facts or some remodeling of principles. A good illustration of this is to be found in the history of the corpuscular and the undulatory hypotheses about light .
Hypothesis n., plural: hypotheses [/haɪˈpɑːθəsɪs/] Definition: Testable scientific prediction
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A scientific hypothesis is a foundational element of the scientific method . It’s a testable statement proposing a potential explanation for natural phenomena. The term hypothesis means “little theory” . A hypothesis is a short statement that can be tested and gives a possible reason for a phenomenon or a possible link between two variables . In the setting of scientific research, a hypothesis is a tentative explanation or statement that can be proven wrong and is used to guide experiments and empirical research.
It is an important part of the scientific method because it gives a basis for planning tests, gathering data, and judging evidence to see if it is true and could help us understand how natural things work. Several hypotheses can be tested in the real world, and the results of careful and systematic observation and analysis can be used to support, reject, or improve them.
Researchers and scientists often use the word hypothesis to refer to this educated guess . These hypotheses are firmly established based on scientific principles and the rigorous testing of new technology and experiments .
For example, in astrophysics, the Big Bang Theory is a working hypothesis that explains the origins of the universe and considers it as a natural phenomenon. It is among the most prominent scientific hypotheses in the field.
“The scientific method: steps, terms, and examples” by Scishow:
Biology definition: A hypothesis is a supposition or tentative explanation for (a group of) phenomena, (a set of) facts, or a scientific inquiry that may be tested, verified or answered by further investigation or methodological experiment. It is like a scientific guess . It’s an idea or prediction that scientists make before they do experiments. They use it to guess what might happen and then test it to see if they were right. It’s like a smart guess that helps them learn new things. A scientific hypothesis that has been verified through scientific experiment and research may well be considered a scientific theory .
Etymology: The word “hypothesis” comes from the Greek word “hupothesis,” which means “a basis” or “a supposition.” It combines “hupo” (under) and “thesis” (placing). Synonym: proposition; assumption; conjecture; postulate Compare: theory See also: null hypothesis
A useful hypothesis must have the following qualities:
Sources of hypothesis are:
One hypothesis is a tentative explanation for an observation or phenomenon. It is based on prior knowledge and understanding of the world, and it can be tested by gathering and analyzing data. Observed facts are the data that are collected to test a hypothesis. They can support or refute the hypothesis.
For example, the hypothesis that “eating more fruits and vegetables will improve your health” can be tested by gathering data on the health of people who eat different amounts of fruits and vegetables. If the people who eat more fruits and vegetables are healthier than those who eat less fruits and vegetables, then the hypothesis is supported.
Hypotheses are essential for scientific inquiry. They help scientists to focus their research, to design experiments, and to interpret their results. They are also essential for the development of scientific theories.
In research, you typically encounter two types of hypothesis: the alternative hypothesis (which proposes a relationship between variables) and the null hypothesis (which suggests no relationship).
It illustrates the association between one dependent variable and one independent variable. For instance, if you consume more vegetables, you will lose weight more quickly. Here, increasing vegetable consumption is the independent variable, while weight loss is the dependent variable.
It exhibits the relationship between at least two dependent variables and at least two independent variables. Eating more vegetables and fruits results in weight loss, radiant skin, and a decreased risk of numerous diseases, including heart disease.
It shows that a researcher wants to reach a certain goal. The way the factors are related can also tell us about their nature. For example, four-year-old children who eat well over a time of five years have a higher IQ than children who don’t eat well. This shows what happened and how it happened.
When there is no theory involved, it is used. It is a statement that there is a connection between two variables, but it doesn’t say what that relationship is or which way it goes.
It says something that goes against the theory. It’s a statement that says something is not true, and there is no link between the independent and dependent factors. “H 0 ” represents the null hypothesis.
When a change in one variable causes a change in the other variable, this is called the associative hypothesis . The causal hypothesis, on the other hand, says that there is a cause-and-effect relationship between two or more factors.
Examples of simple hypotheses:
Examples of a complex hypothesis:
Examples of Directional Hypothesis:
Examples of Non-Directional Hypothesis (or Two-Tailed Hypothesis):
Examples of a null hypothesis:
Examples of Associative Hypothesis:
The research issue can be understood better with the help of a hypothesis, which is why developing one is crucial. The following are some of the specific roles that a hypothesis plays: (Rashid, Apr 20, 2022)
How will Hypothesis help in the Scientific Method?
Research Hypotheses: Did you know that a hypothesis refers to an educated guess or prediction about the outcome of a research study?
It’s like a roadmap guiding researchers towards their destination of knowledge. Just like a compass points north, a well-crafted hypothesis points the way to valuable discoveries in the world of science and inquiry.
Choose the best answer.
Further reading.
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Last updated on September 8th, 2023
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A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.
In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.
In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."
In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.
Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.
Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.
In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.
For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."
An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.
But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."
In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.
Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.
Examples of a hypothesis include:
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Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.
There are 5 main steps in hypothesis testing:
Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.
Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.
After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.
The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.
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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.
There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).
If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.
Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.
Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .
Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.
In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.
In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).
The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .
In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.
In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.
However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.
If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”
These are superficial differences; you can see that they mean the same thing.
You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.
If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Methodology
Research bias
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.
A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).
Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.
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Bevans, R. (2023, June 22). Hypothesis Testing | A Step-by-Step Guide with Easy Examples. Scribbr. Retrieved August 13, 2024, from https://www.scribbr.com/statistics/hypothesis-testing/
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The Difference Between Hypothesis and Theory
A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.
In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.
A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.
A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.
In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.
Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.
The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)
This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.
The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”
While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."
hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.
hypothesis implies insufficient evidence to provide more than a tentative explanation.
theory implies a greater range of evidence and greater likelihood of truth.
law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.
These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.
Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do
1641, in the meaning defined at sense 1a
This is the Difference Between a...
In scientific reasoning, they're two completely different things
hypothermia
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“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 14 Aug. 2024.
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Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.
As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.
In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.
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Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.
The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).
A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart
Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black
Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt
A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)
From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.
The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.
Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.
Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.
Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:
Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.
The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.
A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.
A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.
For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.
For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.
For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.
While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.
It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.
A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.
If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.
This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”
If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.
Hypotheses can be derived from various sources. Some of the sources is given below:
State of knowledge, continuity of research.
Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.
Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.
This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.
An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.
Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.
The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.
Null hypothesis.
The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.
Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.
Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .
Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.
As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .
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A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.
The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.
Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6
Student papers do not include a running head unless requested by the instructor or institution.
Follow the guidelines described next to format each element of the student title page.
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Paper title | Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms. |
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Author names | Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name. | Cecily J. Sinclair and Adam Gonzaga |
Author affiliation | For a student paper, the affiliation is the institution where the student attends school. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author name(s). | Department of Psychology, University of Georgia |
Course number and name | Provide the course number as shown on instructional materials, followed by a colon and the course name. Center the course number and name on the next double-spaced line after the author affiliation. | PSY 201: Introduction to Psychology |
Instructor name | Provide the name of the instructor for the course using the format shown on instructional materials. Center the instructor name on the next double-spaced line after the course number and name. | Dr. Rowan J. Estes |
Assignment due date | Provide the due date for the assignment. Center the due date on the next double-spaced line after the instructor name. Use the date format commonly used in your country. | October 18, 2020 |
| Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header. | 1 |
The professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example.
Follow the guidelines described next to format each element of the professional title page.
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Paper title | Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms. |
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Author names
| Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name. | Francesca Humboldt |
When different authors have different affiliations, use superscript numerals after author names to connect the names to the appropriate affiliation(s). If all authors have the same affiliation, superscript numerals are not used (see Section 2.3 of the for more on how to set up bylines and affiliations). | Tracy Reuter , Arielle Borovsky , and Casey Lew-Williams | |
Author affiliation
| For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center each affiliation on its own line.
| Department of Nursing, Morrigan University |
When different authors have different affiliations, use superscript numerals before affiliations to connect the affiliations to the appropriate author(s). Do not use superscript numerals if all authors share the same affiliations (see Section 2.3 of the for more). | Department of Psychology, Princeton University | |
Author note | Place the author note in the bottom half of the title page. Center and bold the label “Author Note.” Align the paragraphs of the author note to the left. For further information on the contents of the author note, see Section 2.7 of the . | n/a |
| The running head appears in all-capital letters in the page header of all pages, including the title page. Align the running head to the left margin. Do not use the label “Running head:” before the running head. | Prediction errors support children’s word learning |
| Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header. | 1 |
BY ISABELLA BACKMAN August 5, 2024
Promising new research supports that autoimmunity—in which the immune system targets its own body—may contribute to Long COVID symptoms in some patients.
As covered previously in this blog, researchers have several hypotheses to explain what causes Long COVID, including lingering viral remnants, the reactivation of latent viruses, tissue damage, and autoimmunity.
Now, in a recent study , when researchers gave healthy mice antibodies from patients with Long COVID, some of the animals began showing Long COVID symptoms—specifically heightened pain sensitivity and dizziness. It is among the first studies to offer enticing evidence for the autoimmunity hypothesis. The research was led by Akiko Iwasaki, PhD , Sterling Professor of Immunobiology at Yale School of Medicine (YSM).
“We believe this is a big step forward in trying to understand and provide treatment to patients with this subset of Long COVID,” Iwasaki said.
Iwasaki zeroed in on autoimmunity in this study for several reasons. First, Long COVID’s persistent nature suggested that a chronic triggering of the immune system might be at play. Second, women between ages 30 and 50, who are most susceptible to autoimmune diseases, are also at a heightened risk for Long COVID. Finally, some of Iwasaki’s previous research had detected heightened levels of antibodies in people infected with SARS-CoV-2.
Iwasaki’s team isolated antibodies from blood samples obtained from the Mount Sinai-Yale Long COVID study . They transferred these antibodies into mice and then conducted multiple experiments designed to look for changes in behavior that may indicate the presence of specific symptoms. For many of these experiments, mice that received antibodies [the experimental group] behaved no differently than mice that had not [the control group].
However, a few experiments revealed striking changes in the behavior of the experimental mice. These included:
Among the mice that showed behavioral changes, the researchers identified which patients their antibodies came from and what symptoms they had experienced. Interestingly, of the mice that showed heightened pain, 85% received antibodies from patients that reported pain as one of their Long COVID symptoms. Additionally, 89% of mice that had demonstrated loss of balance and coordination on the rotarod test had received antibodies from patients who reported dizziness. Furthermore, 91% of mice that showed reduced strength and muscle weakness received antibodies from patients who reported headache and 55% from patients who reported tinnitus. More research is needed to better understand this correlation.
The autoimmunity hypothesis has recently been further supported by a research group in the Netherlands led by Jeroen den Dunnen, DRS , associate professor at Amsterdam University Medical Center, which also found a link between patients’ Long COVID antibodies and corresponding symptoms in mice.
Diagnosing and treating Long COVID requires doctors to understand what causes the disease. The new study suggests that treatments targeting autoimmunity, such as B cell depletion therapy or plasmapheresis, might alleviate symptoms in some patients by removing the disease-causing antibodies.
Intravenous immunoglobulin (IVIg) is another therapy used for treating autoimmune diseases like lupus in which patients receive antibodies from healthy donors. While its exact mechanism is still unclear, the treatment can help modulate the immune system and reduce inflammation. Could this treatment help cases of Long COVID that are caused by autoimmunity?
A 2024 study led by Lindsey McAlpine, MD , instructor at YSM and first author, and Serena Spudich, MD , Gilbert H. Glaser Professor of Neurology at YSM and principal investigator, found that IVIg might help improve small fiber neuropathy—a condition associated with numbness or painful sensations in the hands and feet—caused by Long COVID. Iwasaki is hopeful that future clinical trials might reveal the benefits of this treatment in helping some of the other painful symptoms of the diseases.
Other drugs are also in the pipeline, such as FcRn inhibitors. FcRn is a receptor that binds to antibodies and recycles them. Blocking this receptor could help bring down levels of circulating antibodies in the blood. An FcRn receptor was recently approved by the FDA for treating myasthenia gravis, another kind of autoimmune disease.
The study could also help researchers create diagnostic tools for evaluating which patients have Long COVID induced by autoimmunity so that doctors can identify who is most likely to benefit from treatments such as these.
Iwasaki plans to continue researching why and how autoantibodies might cause Long COVID, as well as conduct randomized clinical trials on promising treatments. She is also conducting similar antibody transfer studies in other post-acute infection syndromes, such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).
In the meantime, she is excited about her team’s promising results. “Seeing this one-to-one correlation of antibodies that cause pain from patients who reported pain is really gratifying to me as it suggests a causal link,” she says. “It’s a first step, but I think it’s a big one.”
Isabella Backman is associate editor and writer at Yale School of Medicine.
I am very excited by this research, which suggests that at least some of the symptoms of Long COVID are driven by autoimmunity. If so, then this suggests that there may be a way to test for some versions of Long COVID. And if we could identify the patients who have an autoimmune-driven disease, we have treatments to try that have been used with success in other autoimmune diseases. Many of the autoimmune diseases are treated with medications that suppress the immune system. These are powerful medicines that can leave an individual at risk for infection, so they must be thoughtfully applied to patients with evidence of immune system involvement.
I feel as though every blog post here ends with the possibility of better testing and better treatment, but what makes this different is that it points in a very specific direction and leads to the kind of specific questions that help get to useful answers. Which antibodies are involved? Which cells? And finally, can we develop treatments that are specific to those antibodies or to their targets? These are exciting questions, which will, I hope, lead to useful answers.
Read other installments of Long COVID Dispatches here .
If you’d like to share your experience with Long COVID for possible use in a future post (under a pseudonym), write to us at: [email protected]
Information provided in Yale Medicine content is for general informational purposes only. It should never be used as a substitute for medical advice from your doctor or other qualified clinician. Always seek the individual advice of your health care provider for any questions you have regarding a medical condition.
How do the s&p small cap 600 and russell 2000 compare, why invest in small-cap stocks, characteristics of small-cap stocks, how to research small-cap stocks, investment strategies for small-cap stocks, bottom line, frequently asked questions (faqs).
The U.S. stock market appears to be at a key inflection point. Optimism surrounding potential fed interest rate cuts in September is battling recession fears. Adding to the mix is the upcoming U.S. presidential elections, which with its twists and turns is starting to resemble a soap opera. For the most part, investors are ignoring the steady drumbeat of election noise, but market volatility seems to be the prevailing theme. Last week had its worst upheavals as the Dow Jones Industrial Average started the week by diving 1000 points and the S&P 500 fell 3% on a single day, representing a 10% decline from recent highs, while the Nasdaq Composite lost 13% from its 52-week highs. This raised concerns of the higher-for-longer interest rate environment introducing threats to the economy. However, market experts realized that unwinding of yen trades by hedge funds had caused the turmoil. The benchmark indices have made a nice recovery erasing the losses as the week ended. Amid the wild swings in market sentiment, experts see a comeback in small-cap stocks dubbed as the “great rotation,” as these stocks trade at a steeply discounted relative valuation vs. large-caps and stand to benefit most from an interest rate cut. This begs a few crucial questions like:
The general definition of a small-cap is a stock with a market cap between $300 million and $2 billion. The small-cap segment typically has less liquidity and potentially less financial stability than large-caps (which have market caps above $10 billion). The Russell 2000 index, which is considered the home for small-cap stocks, tracks the performance of 1,978 or nearly 2,000 small-cap US stocks. The average market capitalization of the Russell 2000 is $3.4 billion and the median market cap is $996 million (as of July 31). The S&P 600 small-cap index, which tracks 602 small-sized U.S. companies (as of July 31) is more selective in its criteria for including small-caps. For inclusion, stocks should have an unadjusted market cap between $1.0 billion and $6.7 billion and must have positive (as-reported) earnings for the most recent quarter, as well as for the most recent four quarters (summed together). The S&P 600 has a mean market cap of $2.4 billion and median market cap of $2.1 billion.
FTSE Russell 2000 Index Factsheet
S&P 600 factsheet
The Russell 2000 is the broader and more comprehensive index of the small-cap stock universe, but the S&P Small Cap 600 consistently beats the Russell 2000. This superior performance is attributed to the diligent construction of the S&P 600 index that has more stringent requirements for member inclusion.
The S&P 600 screens for companies with positive earnings, which gives it a qualitative edge over the Russell 2000, which has no such criteria.
Russell 2000 reconstitutes the index annually (except in the case of IPOs), and this may be too long a duration for small-cap stocks that are highly volatile. The S&P 600 is more flexible and reconstitutes the index on an as-needed basis, quickly reflecting market conditions, thereby perceived as more timely and accurate. This makes the S&P 600 index returns more stable vs. the Russell 2000.
The Russell 2000 index rose 11% in July, while the S&P Small Cap 600 rose 12%. In comparison, the S&P 500 index edged up less than 1%. This outperformance of the small-caps indices, which is attributed to hopes of Fed rate cuts starting in September, caught the eye of investors, sparking enthusiasm about the “great rotation,” which in the current context refers to a rotation out of mega cap tech stocks with blistering valuations into small-cap stocks.
Logically, a Fed rate cut should favor large-cap stocks as well, so why the excitement around small-caps in particular? This is because small-caps typically have a larger debt burden and more floating-rate debt, thereby more exposed to higher interest rates. So, Fed rate cuts leading to lower borrowing costs are more impactful for smaller companies that tend to rely heavily on debt to fund operations and are highly sensitive to interest rates vs. large-caps.
Yet another corollary is the better performance of small-caps vs. large-caps in a lower interest rate environment. The Russell 2000–the benchmark for the U.S. Small cap stocks–has typically performed well in the past when the Fed policy shifts to interest rate cuts from rate hikes. Over the five Fed rate-cutting cycles since 1990, on average the Russell 2000 has outperformed the S&P 500 by 700 basis points in a 12-month period following the rate cuts.
Forbes. Data from William O’Neil & Co.
Besides Fed rate cut hopes, the compelling valuation of small-cap stocks is a major factor in the great rotation theory. The S&P Small Cap 600 trades at a forward P/E of 16.2x vs. S&P 500’s 21.4x. The Russell 2000 trades at a steep discount to the S&P 500. The relative valuation lingers close to a 37-year low, offering a compelling opportunity. When the valuation spread in the past was huge as the present one, the Russell 2000 had returned almost 40% on average in the following 12 months, according to a research by investment advisory firm Fidelity.
While the “small stocks-big gains” concept is quite alluring with the much-touted high-growth potential for small-caps, there are no guarantees, especially since small-cap stocks are typically characterized by higher volatility, lesser liquidity and lesser financial stability vs. large-cap stocks.
Investing in small-caps can pave the way for huge gains for investors, but the risks of failure can be equally high. Limited access to funding renders the small-caps highly susceptible to market fluctuations.
Most small-cap stocks fly under the radar of investors with low trading volume, meaning reduced availability of buyers and sellers. This can impact buying and selling of such stocks leading to lesser liquidity, implying that it can seriously restrict your ability to make well-timed buys or sells.
Small-caps are typically less profitable with huge debt burdens vs. large-caps that can navigate a challenging high-interest rate environment because of strong earnings growth and pricing power. About 43% of the companies in the Russell 2000 were categorized as loss-making as of June’s end. The Russell 2000 saw a decline of 12.1% in earnings for 2023, but forecasts indicate a potential rebound with a projected 13.4% earnings growth in 2024. This is notably higher than the 10.7% growth expected for the Russell 1000, which tracks large-cap stocks. Meanwhile, the S&P 600 is anticipated to achieve a remarkable 21% year-over-year earnings growth in the fourth quarter of 2024. The lack of any notable catalysts beyond rate cut expectations casts doubts on the feasibility of these ambitious earnings growth forecasts amid slowing economic growth and the fact that the Fed will ease interest rates only gradually.
It’s true that small-caps trade at multi-year low valuations vs. large-caps. However, if weak fundamentals persist and earnings growth fails to meet rosy projections, what was perceived as value could easily turn into a value trap. A discounted valuation alone will not make the case for investing in small-caps, unless the fundamentals recover and grow robustly as projected. Investors looking for growth beyond large-cap techs can still find opportunities in the mid-cap and undervalued large-cap segments that are less-risky investments and equally well positioned as rotation beneficiaries.
That said, small-cap stocks offer significant growth potential. A well-rounded portfolio should include small-cap stocks as well, but it’s crucial to select the right ones and allocate funds based on your individual risk tolerance.
It can be a daunting task to find information on small-cap stocks as many do not have adequate coverage from analysts or financial news/research websites. But, once you identify some stocks, the SEC filings could be a top source of information. Here are some general guidelines.
Investors can invest in small-cap stocks directly or via funds (index funds, exchange-traded funds or mutual funds). Directly investing in small-caps can expose an investor to company-specific risks. If the investor has the skill and time to analyze small cap stocks, investing directly may be the best way to go as it has the potential for higher returns. But for the average investor, investing in small-caps via funds will be a better choice. Then comes up the choice of active or passive managed funds.
For an actively managed fund, a professional fund manager will choose and manage the investments to beat market returns, while a passive fund like an index fund or ETF attempts to replicate returns of a benchmark index (in this case the S&P 600 Small cap Index or the Russell 2000 index) and holds the same securities as that of the index in the same proportion. Returns from a passive fund are aligned with market returns. No surprise there.
With respect to small-cap stocks, actively managed funds have typically beaten passive funds because the volatile nature of small caps require more portfolio intervention. That said, index funds have a cost and tax advantage over active counterparts.
Actively managed funds charge a higher management fee to cover the fund manager’s services, as well as operational expenses, which is reflected in the expense ratio—expressed as a percentage of the fund’s assets under management (AUM). In contrast, passive funds typically have a lower expense ratio since they don’t require active fund management nor do these incur high transaction costs.
Market experts recommend that investors hold small caps for at least 10 years to benefit and allocate 8% of the portfolio to small caps. But this is entirely subject to the risk appetite and investment goals of the investor.
The outlook for small-cap stocks looks increasingly optimistic, driven by anticipated Federal Reserve rate cuts, appealing valuations compared to large-caps and enthusiasm for the Great Rotation. However, sluggish earnings growth still remains a concern for the small-cap segment. Despite forecasts for a significant earnings rebound this year and into 2025, the absence of notable catalysts to support these predictions creates uncertainties. Risk averse investors, at the cost of missing out on any potential gains, may consider avoiding or limiting exposure to small-caps, focusing instead on mid caps or undervalued large-caps, which offer more stability and lesser risk. Small-cap stocks are more suitable for growth investors with a robust risk appetite.
Please note that I am not a registered investment advisor and readers should do their own due diligence before investing in this or any other stock. I am not responsible for the investment decisions made by individuals after reading this article. Readers are asked not to rely on the opinions and analysis expressed in the article and encouraged to do their own research before investing.
A small-cap stock is publicly traded with a market cap typically ranging between $300 million and $2 billion.
Yes, small-caps are more volatile and highly sensitive to the economic climate compared to large caps that have the wherewithal to weather economic downturns.
If you have a brokerage account, you can start investing in small-cap stocks directly, especially if you have the skills and time for researching these high-risk-reward stocks. You can also invest through index funds and ETFs that track the small-cap indexes S&P Small Cap 600 and the Russell 2000.
Financial/investment news websites, SEC filings, company investor relation resources, investment reports and brokerage platforms.
Small-cap stocks are volatile and risky in nature, therefore not very suitable for a retirement portfolio, which would typically include safer income-generating investments like dividend stocks/index funds/ETFs, etc.
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How did life begin on earth a lightning strike of an idea..
Yahya Chaudhry
Harvard Correspondent
About four billion years ago, Earth resembled the set of a summer sci-fi blockbuster. The planet’s surface was a harsh and barren landscape, recovering from hellish asteroid strikes, teeming with volcanic eruptions, and lacking enough nutrients to sustain even the simplest forms of life.
The atmosphere was composed predominantly of inert gases like nitrogen and carbon dioxide, meaning they did not easily engage in chemical reactions necessary to form the complex organic molecules that are the building blocks of life. Scientists have long sought to discover the key factors that enabled the planet’s chemistry to change enough to form and sustain life.
Now, new research zeroes in on how lightning strikes may have served as a vital spark, transforming the atmosphere of early Earth into a hotbed of chemical activity. In the study, published in Proceedings of the National Academy of Sciences , a team of Harvard scientists identified lightning-induced plasma electrochemistry as a potential source of reactive carbon and nitrogen compounds necessary for the emergence and survival of early life.
“The origin of life is one of the great unanswered questions facing chemistry,” said George M. Whitesides, senior author and the Woodford L. and Ann A. Flowers University Research Professor in the Department of Chemistry and Chemical Biology. How the fundamental building blocks of “nucleic acids, proteins, and metabolites emerged spontaneously remains unanswered.”
One of the most popular answers to this question is summarized in the so-called RNA World hypothesis, Whitesides said. That is the idea that available forms of the elements, such as water, soluble electrolytes, and common gases, formed the first biomolecules. In their study, the researchers found that lightning could provide accessible forms of nitrogen and carbon that led to the emergence and survival of biomolecules.
A plasma vessel used to mimic cloud-to-ground lightning and its resulting electrochemical reactions. The setup uses two electrodes, with one in the gas phase and the other submerged in water enriched with inorganic salts.
Credit: Haihui Joy Jiang
Researchers designed a plasma electrochemical setup that allowed them to mimic conditions of the early Earth and study the role lightning strikes might have had on its chemistry. They were able to generate high-energy sparks between gas and liquid phases — akin to the cloud-to-ground lightning strikes that would have been common billions of years ago.
The scientists discovered that their simulated lightning strikes could transform stable gases like carbon dioxide and nitrogen into highly reactive compounds. They found that carbon dioxide could be reduced to carbon monoxide and formic acid, while nitrogen could be converted into nitrate, nitrite, and ammonium ions.
These reactions occurred most efficiently at the interfaces between gas, liquid, and solid phases — regions where lightning strikes would naturally concentrate these products. This suggests that lightning strikes could have locally generated high concentrations of these vital molecules, providing diverse raw materials for the earliest forms of life to develop and thrive.
“Given what we’ve shown about interfacial lightning strikes, we are introducing different subsets of molecules, different concentrations, and different plausible pathways to life in the origin of life community,” said Thomas C. Underwood, co-lead author and Whitesides Lab postdoctoral fellow. “As opposed to saying that there’s one mechanism to create chemically reactive molecules and one key intermediate, we suggest that there is likely more than one reactive molecule that might have contributed to the pathway to life.”
The findings align with previous research suggesting that other energy sources, such as ultraviolet radiation, deep-sea vents, volcanoes, and asteroid impacts, could have also contributed to the formation of biologically relevant molecules. However, the unique advantage of cloud-to-ground lightning is its ability to drive high-voltage electrochemistry across different interfaces, connecting the atmosphere, oceans, and land.
The research adds a significant piece to the puzzle of life’s origins. By demonstrating how lightning could have contributed to the availability of essential nutrients, the study opens new avenues for understanding the chemical pathways that led to the emergence of life on Earth. As the research team continues to explore these reactions, they hope to uncover more about the early conditions that made life possible and to improve modern applications.
“Building on our work, we are now experimentally looking at how plasma electrochemical reactions may influence nitrogen isotopes in products, which has a potential geological relevance,” said co-lead author Haihui Joy Jiang, a former Whitesides lab postdoctoral fellow. “We are also interested in this research from an energy-efficiency and environmentally friendly perspective on chemical production. We are studying plasma as a tool to develop new methods of making chemicals and to drive green chemical processes, such as producing fertilizer used today.”
Harvard co-authors included Professor Dimitar D. Sasselov in the Department of Astronomy and Professor James G. Anderson in the Department of Chemistry and Chemical Biology, Department of Earth and Planetary Sciences, and the Harvard John A. Paulson School of Engineering and Applied Sciences.
The study not only sheds light on the past but also has implications for the search for life on other planets. Processes the researchers described could potentially contribute to the emergence of life beyond Earth.
“Lightning has been observed on Jupiter and Saturn; plasmas and plasma-induced chemistry can exist beyond our solar system,” Jiang said. “Moving forward, our setup is useful for mimicking environmental conditions of different planets, as well as exploring reaction pathways triggered by lightning and its analogs.”
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- Community Bank Financial Performance | |
- Structural Change Among Community and Noncommunity Banks | |
- The Effects of Demographic Changes on Community Banks | |
- Notable Lending Strengths of Community Banks | |
- Regulatory Change and Community Banks | |
- Technology in Community Banks | |
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- Study Definitions | |
- Selected Federal Agency Actions Affecting Community Banks, 2008–2019 | |
FDIC Targeted Research on Community Banks - As part of the overall Community Banking Study, the FDIC conducted targeted research about specific issues and questions related to community banks. Staff Studies Report No. 2020-06 Using financial and supervisory data from the past 20 years, we show that scale economies in community banks with less than $10 billion in assets emerged during the run-up to the 2008 financial crisis due to declines in interest expenses and provisions for losses on loans and leases at larger banks. The financial crisis temporarily interrupted this trend and costs increased industry-wide, but a generally more cost-efficient industry re-emerged, returning in recent years to pre-crisis trends. We estimate that from 2000 to 2019, the cost-minimizing size of a bank’s loan portfolio rose from approximately $350 million to $3.3 billion. Though descriptive, our results suggest efficiency gains accrue early as a bank grows from $10 million in loans to $3.3 billion, with 90 percent of the potential efficiency gains occurring by $300 million. | |
- The FDIC Community Banking study relies on a comprehensive database that enables consistent analysis across the industry beginning in 1984. |
The FDIC's 2012 Community Banking Study is a data-driven effort to identify and explore issues and questions about community banks. This study is intended to be foundational, providing a platform for future research and analysis by the FDIC and other interested parties.. All items below are PDF files. See for assistance. |
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- Defining the Community Bank |
- Structural Change Among Community and Noncommunity Banks |
- The Geography of Community Banks |
- Comparative Financial Performance: Community versus Noncommunity Banks |
- Comparative Performance of Community Bank Lending Specialty Groups |
- Capital Formation at Community Banks |
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- Details of the Research Definition of the Community Bank |
- Regulatory Compliance Costs - A Summary of Interviews with Community Bankers |
FDIC Targeted Research on Community Banks - As part of the overall Community Banking Study, the FDIC conducted targeted research about specific issues and questions related to community banks. This paper examines efficiency ratio trends to identify the underlying reasons for the performance game between community and noncommunitybanks. The paper also estimates the importance of economies of scale among community banks. This paper controls for underlying economic conditions to identify bank-specific factors that affect community bank performance. |
- The FDIC Community Banking study relies on a comprehensive database that enables consistent analysis across the industry beginning in 1984. |
Last Updated: June 2, 2022
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A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes - specificity, clarity and testability. Let's take a look at these more closely.
Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation. Characteristics of a good hypothesis
Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
What they need at the start of their research is to formulate a scientific hypothesis that revisits conventional theories, real-world processes, and related evidence to propose new studies and test ideas in an ethical way.3 Such a hypothesis can be of most benefit if published in an ethical journal with wide visibility and exposure to relevant ...
hypothesis. science. scientific hypothesis, an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an "If…then" statement summarizing the idea and in the ...
A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation ('x affects y because …'). A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses.
The hypothesis of Andreas Cellarius, showing the planetary motions in eccentric and epicyclical orbits. A hypothesis (pl.: hypotheses) is a proposed explanation for a phenomenon.For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with ...
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
HYPOTHESIS TESTING. A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the "alternate" hypothesis, and the opposite ...
hypothesis, something supposed or taken for granted, with the object of following out its consequences (Greek hypothesis, "a putting under," the Latin equivalent being suppositio ). Discussion with Kara Rogers of how the scientific model is used to test a hypothesis or represent a theory. Kara Rogers, senior biomedical sciences editor of ...
A hypothesisis a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence. Within social science, a hypothesis can ...
A hypothesis is a short statement that can be tested and gives a possible reason for a phenomenon or a possible link between two variables. In the setting of scientific research, a hypothesis is a tentative explanation or statement that can be proven wrong and is used to guide experiments and empirical research. Figure 1: What is Hypothesis.
A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject. In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.
Step 5: Present your findings. The results of hypothesis testing will be presented in the results and discussion sections of your research paper, dissertation or thesis.. In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p-value).
A hypothesis is often called an "educated guess," but this is an oversimplification. An example of a hypothesis would be: "If snake species A and B compete for the same resources, and if we ...
A hypothesis is a statement of the researcher's expectation or prediction about relationship among study variables. The research process begins and ends with the hypothesis. It is core to the ...
hypothesis: [noun] an assumption or concession made for the sake of argument. an interpretation of a practical situation or condition taken as the ground for action.
A hypothesis is a concept or idea that you test through research and experiments. In other words, it is a prediction that is can be tested by research. Most researchers come up with a hypothesis statement at the beginning of the study. Thus basically, you make a prediction about the outcome at the start of the study and conduct experiments to ...
Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.
For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center ...
It is among the first studies to offer enticing evidence for the autoimmunity hypothesis. The research was led by Akiko Iwasaki, PhD, Sterling Professor of Immunobiology at Yale School of Medicine (YSM). "We believe this is a big step forward in trying to understand and provide treatment to patients with this subset of Long COVID," Iwasaki ...
The general definition of a small-cap is a stock with a market cap between $300 million and $2 billion. ... differentiated solutions and research its suppliers and other agents that can equally ...
Now, new research zeroes in on how lightning strikes may have served as a vital spark, transforming the atmosphere of early Earth into a hotbed of chemical activity. ... One of the most popular answers to this question is summarized in the so-called RNA World hypothesis, Whitesides said. That is the idea that available forms of the elements ...
Note: On June 1, 2022, the text in Appendix A: Study Definitions (p. A-1) was modified slightly from the original online version posted on December 15, 2020, and from the printed version. The wording for the second item in the "Exclude" section was changed to "Assets held in foreign branches >=10% of total assets" from "Foreign Assets ...