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In scientific research, the null hypothesis (often denoted H 0) [1] is the claim that the effect being studied does not exist. [note 1] The null hypothesis can also be described as the hypothesis in which no relationship exists between two sets of data or variables being analyzed. If the null hypothesis is true, any experimentally observed ...
In statistical hypothesis testing, a null result occurs when an experimental result is not significantly different from what is to be expected under the null hypothesis; its probability (under the null hypothesis) does not exceed the significance level, i.e., the threshold set prior to testing for rejection of the null hypothesis.
A one-sample Student's t-test is a location test of whether the mean of a population has a value specified in a null hypothesis. In testing the null hypothesis that the population mean is equal to a specified value μ 0, one uses the statistic = ¯ /,
This is why the hypothesis under test is often called the null hypothesis (most likely, coined by Fisher (1935, p. 19)), because it is this hypothesis that is to be either nullified or not nullified by the test. When the null hypothesis is nullified, it is possible to conclude that data support the "alternative hypothesis" (which is the ...
In statistics, Welch's t-test, or unequal variances t-test, is a two-sample location test which is used to test the (null) hypothesis that two populations have equal means. It is named for its creator, Bernard Lewis Welch , and is an adaptation of Student's t -test , [ 1 ] and is more reliable when the two samples have unequal variances and ...
Type I error: "rejecting the null hypothesis when it is true". Type II error: "failing to reject the null hypothesis when it is false". Type III error: "correctly rejecting the null hypothesis for the wrong reason". (1948, p. 61) [c]
Thus, the null hypothesis is rejected if >, (where , is the upper tail critical value for the distribution). Bartlett's test is a modification of the corresponding likelihood ratio test designed to make the approximation to the χ k − 1 2 {\displaystyle \chi _{k-1}^{2}} distribution better (Bartlett, 1937).
The null hypothesis is that the subject has no ability to distinguish the teas. In Fisher's approach, there was no alternative hypothesis , [ 2 ] unlike in the Neyman–Pearson approach . The test statistic is a simple count of the number of successful attempts to select the four cups prepared by a given method.