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Statistical hypothesis testing is a key technique of both frequentist inference and Bayesian inference, although the two types of inference have notable differences. Statistical hypothesis tests define a procedure that controls (fixes) the probability of incorrectly deciding that a default position (null hypothesis) is incorrect. The procedure ...
Henry Scheffé's simultaneous test of all contrasts in multiple comparison problems is the most [citation needed] well-known remedy in the case of analysis of variance. [1] It is a method designed for testing hypotheses suggested by the data while avoiding the fallacy described above.
Production of a small p-value by multiple testing. 30 samples of 10 dots of random color (blue or red) are observed. On each sample, a two-tailed binomial test of the null hypothesis that blue and red are equally probable is performed. The first row shows the possible p-values as a function of the number of blue and red dots in the sample.
In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant .
The process discourages the analyst from choosing one "likely" hypothesis and using evidence to prove its accuracy. Cognitive bias is minimized when all possible hypotheses are considered. [1] Evidence – The analyst then lists evidence and arguments (including assumptions and logical deductions) for and against each hypothesis. [1]
Test statistic is a quantity derived from the sample for statistical hypothesis testing. [1] A hypothesis test is typically specified in terms of a test statistic, considered as a numerical summary of a data-set that reduces the data to one value that can be used to perform the hypothesis test.
The hypothetico-deductive model or method is a proposed description of the scientific method.According to it, scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable, using a test on observable data where the outcome is not yet known.
A statistical significance test is intended to test a hypothesis. If the hypothesis summarizes a set of data, there is no value in testing the hypothesis on that set of data. Example: If a study of last year's weather reports indicates that rain in a region falls primarily on weekends, it is only valid to test that null hypothesis on weather ...
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