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  2. A t test is a statistical hypothesis test that assesses sample means to draw conclusions about population means. Frequently, analysts use a t test to determine whether the population means for two groups are different.

  3. The t test tells you how significant the differences between group means are. It lets you know if those differences in means could have happened by chance. The t test is usually used when data sets follow a normal distribution but you don’t know the population variance.

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  4. The t-test calculates the “t-statistic” or “t-value” based on the two groups' means, standard deviations, and sample sizes. This t-value is then compared to the critical value—the point in the data where you’d reject the null hypothesis and say there is no significant difference—to decide whether the difference is significant.

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  5. A t test is a statistical technique used to quantify the difference between the mean (average value) of a variable from up to two samples (datasets). The variable must be numeric. Some examples are height, gross income, and amount of weight lost on a particular diet.

  6. This t-test calculator allows you to use either the p-value approach or the critical regions approach to hypothesis testing! Enter your T-score and the number of degrees of freedom . If you don't know them, provide some data about your sample(s): sample size, mean, and standard deviation, and our t-test calculator will compute the T-score and ...

  7. Depending on the t-test that you use, you can compare a sample mean to a hypothesized value, the means of two independent samples, or the difference between paired samples. In this post, I show you how t-tests use t-values and t-distributions to calculate probabilities and test hypotheses.

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