Assumptions for the t-test:

  1. 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.
  2. 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.
  3. 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.
  4. Random Sampling
    • Data should come from a random sample, ensuring that every individual has an equal chance of being included in the study.
  5. 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.
  6. 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.

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