In statistics, a **null hypothesis** is a hypothesis set up to be nullified or refuted in order to support an *alternative hypothesis*. When used, the null hypothesis is presumed true until statistical evidence in the form of a hypothesis test indicates otherwise. The use of the null hypothesis is controversial, a null hypothesis is often the reverse of what the experimenter actually believes; it is put forward to allow the data to contradict it.(see papers linked below) Template:Otherusescccc A graph of a bell curve in a normal distribution showing statistics used in educational assessment, comparing various grading methods. ...
In statistics, the Alternative Hypothesis is the hypothesis proposed to explain a statistically significant difference between results, that is if the Null Hypothesis has been rejected. ...
Evidence has several meanings as indicated below. ...
## Introduction
The null hypothesis is generally that which is presumed to be true initially. Hence, we reject only when we are quite sure that it is false, often 90, 95, or 99% confident that the data do not support it.
## An example For example, if we want to compare the test scores of two random samples of men and women, a null hypothesis would be that the mean score of the male population was the same as the mean score of the female population: A sample is that part of a population which is actually observed. ...
*H*_{0} : μ_{1} = μ_{2} where: *H*_{0} = the null hypothesis - μ
_{1} = the mean of population 1, and - μ
_{2} = the mean of population 2. Alternatively, the null hypothesis can postulate that the two samples are drawn from the same population, so that the variance and shape of the distributions are equal, as well as the means. Formulation of the null hypothesis is a vital step in testing statistical significance. Having formulated such a hypothesis, one can establish the probability of observing the obtained data or data more different from the prediction of the null hypothesis, if the null hypothesis is true. That probability is what is commonly called the "significance level" of the results. In statistics, a result is significant if it is unlikely to have occurred by chance, given that a presumed null hypothesis is true. ...
When a null hypothesis is formed, it is always in contrast to an implicit *alternative hypothesis*, which is accepted if the observed data values are sufficiently improbable under the null hypothesis. The precise formulation of the null hypothesis has implications for the alternative. For example, if the null hypothesis is that sample A is drawn from a population with the same mean as sample B, the alternative hypothesis is that they come from populations with *different* means, which can be tested with a two-tailed test of significance. But if the null hypothesis is that sample A is drawn from a population whose mean is *lower* than the mean of the population from which sample B is drawn, the alternative hypothesis is that sample A comes from a population with a *higher* mean than the population from which sample B is drawn, which can be tested with a one-tailed test. The two-tailed test is the test of a given statistical hypothesis in which a value of the statistic that is either sufficiently small or sufficiently large will lead to rejection of the hypothesis tested. ...
## Limitations A null hypothesis is only useful if it is possible to calculate the probability of observing a data set with particular parameters from it. In general it is much harder to be precise about how probable the data would be if the alternative hypothesis is true. If experimental observations contradict the prediction of the null hypothesis, it means that either the null hypothesis is false, or we have observed an event with very low probability. This gives us high confidence in the falsehood of the null hypothesis, which can be improved by increasing the number of trials. However, accepting the alternative hypothesis only commits us to a difference in observed parameters; it does not prove that the theory or principles that predicted such a difference is true, since it is always possible that the difference could be due to additional factors not recognised by the theory. For example, rejection of a null hypothesis (that, say, rates of symptom relief in a sample of patients who received a placebo and a sample who received a medicinal drug will be equal) allows us to make a non-null statement (that the rates differed); it does not prove that the drug relieved the symptoms, though it gives us more confidence in that hypothesis. // A placebo is a medicine or preparation which has no inherent pertinent pharmacologic activity but which is effective only by virtue of the factor of suggestion attendant upon its administration. ...
The formulation, testing, and rejection of null hypotheses is methodologically consistent with the falsificationist model of scientific discovery formulated by Karl Popper and widely believed to apply to most kinds of empirical research. However, concerns regarding the high power of statistical tests to detect differences in large samples have led to suggestions for re-defining the null hypothesis, for example as a hypothesis that an effect falls within a range considered negligible. This is an attempt to address the confusion among non-statisticians between *significant* and *substantial*, since large enough samples are likely to be able to indicate differences however minor. This page discusses how a theory or assertion is falsifiable (disprovable opp: verifiable), rather than the non-philosophical use of falsification, meaning counterfeiting. ...
Part of a scientific laboratory at the University of Cologne. ...
Sir Karl Raimund Popper, CH, MA, Ph. ...
Empirical research is any activity that uses direct or indirect observation as its test of reality. ...
The power of a statistical test is the probability that the test will reject a false null hypothesis, or in other words that it will not make a Type II error. ...
One may be faced with the problem of making a definite decision with respect to an uncertain hypothesis which is known only through its observable consequences. ...
The theory underlying the idea of a null hypothesis is closely associated with the frequentist theory of probability, in which probabilistic statements can only be made about the relative frequencies of events in arbitrarily large samples. A failure to reject the null hypothesis is meaningful only in relation to an arbitrarily large population from which the observed sample is supposed to be drawn. Statistical regularity has motivated the development of the relative frequency concept of probability. ...
## Publication bias See more detail at publication bias. Publication bias, also called the positive outcome bias, is typically the tendency for researchers to publish experimental results that have a positive result (found something), while consequently not publishing findings which have a negative result (found that something did not happen). ...
In 2002, a group of psychologists launched a new journal dedicated to experimental studies in psychology which support the null hypothesis. The *Journal of Articles in Support of the Null Hypothesis* (JASNH) was founded to address a scientific publishing bias against such articles. [1] According to the editors, For album titles with the same name, see 2002 (album). ...
Psychology is an academic and applied discipline involving the scientific study of mental processes and behavior. ...
- "other journals and reviewers have exhibited a bias against articles that did not reject the null hypothesis. We plan to change that by offering an outlet for experiments that do not reach the traditional significance levels (p < 0.05). Thus, reducing the file drawer problem, and reducing the bias in psychological literature. Without such a resource researchers could be wasting their time examining empirical questions that have already been examined. We collect these articles and provide them to the scientific community free of cost."
The "File Drawer problem" is a problem that exists due to the fact that academics tend not to publish results that indicate the null hypothesis could not be rejected. That is, they got a statistically significant result that indicated the relationship they were looking for did not exist. Even though these papers can often be interesting, they tend to end up unpublished, in "file drawers." Ioannidis has inventoried factors that should alert readers to risks of publication bias ^{[1]}.
## Controversy Null hypothesis testing has always been controversial. Many statisticians have pointed out that rejecting the null hypothesis says nothing or very little about the likelihood that the null is true. Under traditional null hypothesis testing, the null is rejected when P(Data | Null)^{†} is very small, say 0.05. However, researchers are really interested in P(Null | Data) which cannot be inferred from a p-value. In some cases, P(Null | Data) approaches 1 while P(Data | Null) approaches 0, in other words, we can reject the null when it's virtually certain to be true. For this and other reasons, Gerd Gigerenzer has called null hypothesis testing "mindless statistics" while Jacob Cohen describes it as a ritual conducted to convince ourselves that we have the evidence needed to confirm our theories. In statistical hypothesis testing, the p-value of a random variable T used as a test statistic is the probability that T will assume a value at least as extreme as the observed value tobserved, given that a null hypothesis being considered is true. ...
Gerd Gigerenzer (b. ...
Elizabeth Anscombe, a student of Wittgenstein, notes that “Tests of the null hypothesis that there is no difference between certain treatments are often made in the analysis of agricultural or industrial experiments in which alternative methods or processes are compared. Such tests are [...] totally irrelevant. What are needed are estimates of magnitudes of effects, with standard errors." Gertrude Elizabeth Margaret Anscombe (March 18, 1919 â€“ January 5, 2001) (known as Elizabeth Anscombe, published as G. E. M. Anscombe) was a British analytic philosopher, a theologian and a pupil of Ludwig Wittgenstein. ...
Ludwig Wittgenstein (1889-1951), pictured here in 1930, made influential contributions to Logic and the philosophy of language, critically examining the task of conventional philosophy and its relation to the nature of language. ...
Bayesian statisticians normally reject the idea of null hypothesis testing. Given a prior probability distribution for one or more parameters, sample evidence can be used to generate an updated posterior distribution. In this framework, but *not* in the null hypothesis testing framework, it is meaningful to make statements of the general form "the probability that the true value of the parameter is greater than 0 is p". Bayesian refers to probability and statistics -- either methods associated with the Reverend Thomas Bayes (ca. ...
A prior probability is a marginal probability, interpreted as a description of what is known about a variable in the absence of some evidence. ...
In Bayesian probability theory, the posterior probability is the conditional probability of some event or proposition, taking empirical data into account. ...
^{†}(Read: the probability of observing the particular data given that the null hypothesis is true; see conditional probability.) This article defines some terms which characterize probability distributions of two or more variables. ...
## References HyperStat Online - http://davidmlane.com/hyperstat/A29337.html **^** Ioannidis J (2005). "Why most published research findings are false". *PLoS Med* **2** (8): e124. DOI:10.1371/journal.pmed.0020124. PMID 16060722. A digital object identifier (or DOI) is a standard for persistently identifying a piece of intellectual property on a digital network and associating it with related data, the metadata, in a structured extensible way. ...
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