A t-test is a statistical test used to compare the means of two groups to determine if they are significantly different from each other. It is commonly used in hypothesis testing to assess whether a process, treatment, or difference between groups has a real effect or if the observed difference could be due to chance
. Key points about the t-test:
- It compares the means of two samples and calculates a t-value, which measures the magnitude of the difference relative to the variability in the data
- The test assumes the data are independent, approximately normally distributed, and have similar variances (homogeneity of variance)
- There are different types of t-tests:
- One-sample t-test : compares the mean of one group to a known standard or value.
- Two-sample (independent) t-test : compares the means of two independent groups.
- Paired t-test : compares means from the same group at two different times or under two conditions (dependent samples)
- The t-test results in a p-value that indicates the probability that the observed difference is due to chance. A low p-value suggests a statistically significant difference
- It is suitable only for comparing two groups; for more than two groups, other tests like ANOVA should be used
- The t-test is especially useful for small sample sizes (under 30), where normal distribution assumptions are important
In summary, the t-test is a fundamental inferential statistical method used to test hypotheses about the difference between two means, helping researchers decide if observed differences are statistically meaningful or likely due to random variation
