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Assumptions for the t-test:
- Normality
- The data for each group should be approximately normally distributed.
- This assumption can be checked using various methods, such as histograms, Q-Q plots, or statistical tests like the Shapiro-Wilk test.
- Homogeneity of Variances
- The variances of the two groups should be equal.
- This is especially important for the independent two-sample t-test.
- Can be checked using the Levene’s test.
- Independent Observations
- The observations (or data points) in each group should be independent of each other.
- This typically means that one observation in a group should not influence another observation.
- Random Sampling
- Data should come from a random sample, ensuring that every individual has an equal chance of being included in the study.
- Scale of Measurement
- The t-test is appropriate for continuous (interval or ratio) data.
- The dependent variable should be continuous, while the independent variable should be categorical with two levels/groups.
- Absence of Outliers
- Outliers can significantly affect the mean and standard deviation, which in turn can affect the t-test results.
- It’s important to check for outliers and decide how to handle them before conducting the t-test.
