Use this interactive t-table to find one-tailed and two-tailed t critical values by significance level and degrees of freedom. Learn how to read the table. Learn to run a t-test in R three ways: one-sample (against a known mean), independent two-sample (Student and Welch), and paired. A confidence interval is a range of values used to estimate an unknown population parameter, such as the mean or proportion. It shows how much uncertainty is associated with a sample estimate. In essence, equivalence tests consist of calculating a confidence interval around an observed effect size and rejecting effects more extreme than the equivalence bound when the confidence interval does not overlap with the equivalence bound. The darker shaded region represents the 90% confidence interval, while the lighter region represents the 95% confidence interval. This visualization helps illustrate the precision of our estimate. a logical variable indicating whether to treat the two variances as being equal. If TRUE then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used. This tutorial explains how to perform a two sample t-test in R, including a complete example. Performs equivalence testing using the Two One-Sided Tests (TOST) procedure with bootstrapped log-transformed t-tests. This approach is particularly useful for ratio-scale data where the equiva-lence bounds are expressed as ratios (e.g., bioequivalence studies). The third thing to note is that the confidence interval is different too: it now reports a “one-sided” confidence interval rather than a two-sided one. In a two-sided confidence interval, we’re trying to find numbers a and b such that we’re 95% confident that the true mean lies between a and b. The function allows you to conduct various types of t-tests, such as one-sample t-test, independent samples t-test and paired samples t-test, for equal or different variances.