Wow, my constant desire to learn new things has really put me into a deep deep hole. After clicking on this link via bento-box I experienced a moment on Non zen thought processes and I will certainly have to spend some time researching this topic.
A t test is any statistical hypothesis test in which the test statistic has a Student’s t distribution if the null hypothesis is true. Among the most frequently used t tests are:
- A test of the null hypothesis that the means of two normally distributed populations are equal. Given two data sets, each characterized by its mean, standard deviation and number of data points, we can use some kind of t test to determine whether the means are distinct, provided that the underlying distributions can be assumed to be normal. All such tests are usually called Student’s t tests, though strictly speaking that name should only be used if the variances of the two populations are also assumed to be equal; the form of the test used when this assumption is dropped is sometimes called Welch’s t test. There are different versions of the t test depending on whether the two samples are
- independent of each other (e.g., individuals randomly assigned into two groups), or
- paired, so that each member of one sample has a unique relationship with a particular member of the other sample (e.g., the same people measured before and after an intervention, or IQ test scores of a husband and wife).
- If the P value that is calculated is less than the threshold chosen for statistical significance (usually the 0.05 level), then the null hypothesis that the two groups do not differ is rejected in favor of the alternative hypothesis, which typically states that the groups do differ.
- A test of whether the mean of a normally distributed population has a value specified in a null hypothesis.
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- A test of whether the slope of a regression line differs significantly from 0.
