You should choose the statistical test that best fits your data and matches the effect or relationship you want to test. Different statistical tests have different assumptions and generate different test statistics.The calculation of the p-value depends on the statistical test you are using to test your hypothesis:
These tables show, based on the test statistic and degrees of freedom (number of observations minus number of independent variables) of your test, how frequently you would expect to see that test statistic under the null hypothesis. You can also find tables for estimating the p-value of your test statistic online. P-values are usually automatically calculated by your statistical program (R, SPSS, etc.). The p-value will never reach zero, because there’s always a possibility, even if extremely unlikely, that the patterns in your data occurred by chance. If, however, there is an average difference in longevity between the two groups, then your test statistic will move further away from the values predicted by the null hypothesis, and the p-value will get smaller. It likely won’t reach exactly 1, because in real life the groups will probably not be perfectly equal. Example: Test statistic and p-valueIf the mice live equally long on either diet, then the test statistic from your t-test will closely match the test statistic from the null hypothesis (that there is no difference between groups), and the resulting p-value will be close to 1. The p-value is a proportion: if your p-value is 0.05, that means that 5% of the time you would see a test statistic at least as extreme as the one you found if the null hypothesis was true. The p-value gets smaller as the test statistic calculated from your data gets further away from the range of test statistics predicted by the null hypothesis. The p-value tells you how often you would expect to see a test statistic as extreme or more extreme than the one calculated by your statistical test if the null hypothesis of that test was true. It does this by calculating the likelihood of your test statistic, which is the number calculated by a statistical test using your data. The p -value, or probability value, tells you how likely it is that your data could have occurred under the null hypothesis. Alternative hypothesis: there is a difference in longevity between the two groups.Null hypothesis: there is no difference in longevity between the two groups.You can statistically test the difference between these two diets using a two-tailed t test. Example: Null and alternative hypothesisYou want to know whether there is a difference in longevity between two groups of mice fed on different diets, diet A and diet B. For most tests, the null hypothesis is that there is no relationship between your variables of interest or that there is no difference among groups.įor example, in a two-tailed t-test, the null hypothesis is that the difference between two groups is zero. Frequently asked questions about p-valuesĪll statistical tests have a null hypothesis.
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