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p. -value. In null-hypothesis significance testing, the -value[note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. [2][3] A very small p -value means that such an extreme observed outcome would be very unlikely under the null hypothesis.
Statistical significance. In statistical hypothesis testing, [1][2] a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. [3] More precisely, a study's defined significance level, denoted by , is the probability of the study rejecting the null hypothesis, given that ...
On the other hand, if the p value is greater than the chosen alpha level, then the null hypothesis (that the data came from a normally distributed population) can not be rejected (e.g., for an alpha level of .05, a data set with a p value of less than .05 rejects the null hypothesis that the data are from a normally distributed population ...
A statistical hypothesis test typically involves a calculation of a test statistic. Then a decision is made, either by comparing the test statistic to a critical value or equivalently by evaluating a p -value computed from the test statistic. Roughly 100 specialized statistical tests have been defined. [ 1 ][ 2 ]
Data dredging. Data dredging (also known as data snooping or p-hacking) [1][a] is the misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives.
Fisher's exact test is a statistical significance test used in the analysis of contingency tables. [1][2][3] Although in practice it is employed when sample sizes are small, it is valid for all sample sizes. It is named after its inventor, Ronald Fisher, and is one of a class of exact tests, so called because the significance of the deviation ...
A two-tailed test applied to the normal distribution. A one-tailed test, showing the p -value as the size of one tail. In statistical significance testing, a one-tailed test and a two-tailed test are alternative ways of computing the statistical significance of a parameter inferred from a data set, in terms of a test statistic.
Under Fisher's method, two small p-values P 1 and P 2 combine to form a smaller p-value.The darkest boundary defines the region where the meta-analysis p-value is below 0.05.. For example, if both p-values are around 0.10, or if one is around 0.04 and one is around 0.25, the meta-analysis p-value is around 0