We want to know if the mean score for the population of native English speakers is different from the people who learned English as a second language. Our idea is that the mean test scores for the underlying populations of native and non-native English speakers are not the same. Our two groups are the native English speakers and the non-native speakers. We have students who speak English as their first language and students who do not.We also have an idea, or hypothesis, that the means of the underlying populations for the two groups are different. The second variable is the measurement of interest. When you cannot safely assume normality, you can perform a nonparametric test that doesn’t assume normality.įor the two-sample t-test, we need two variables. You might need to rely on your understanding of the data. If your sample sizes are very small, you might not be able to test for normality. What if my data isn’t nearly normally distributed?

You use a different estimate of the standard deviation. What if the variances for my two groups are not equal? Other multiple comparison methods include the Tukey-Kramer test of all pairwise differences, analysis of means (ANOM) to compare group means to the overall mean or Dunnett’s test to compare each group mean to a control mean. Analysis of variance (ANOVA) is one such method. You can use the test when your data values are independent, are randomly sampled from two normal populations and the two independent groups have equal variances. Yes, a two-sample t-test is used to analyze the results from A/B tests. The two-sample t-test (also known as the independent samples t-test) is a method used to test whether the unknown population means of two groups are equal or not.

What does a high sample variance mean