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To perform a t-test when the sample size is small, the sample must show no evidence of strong skewness and normality.
Here's why:
1. Skewness refers to the lack of symmetry in the distribution of the data. If the sample shows strong skewness, it indicates that the data is not normally distributed, which is a requirement for performing a t-test.
2. Normality is crucial for t-tests as they assume that the data follows a normal distribution. If the sample does not exhibit normality, it can lead to biased results when conducting a t-test with a small sample size.
Therefore, when performing a t-test with a small sample size, it is essential for the sample data to demonstrate no evidence of strong skewness and to follow a normal distribution for the results to be reliable and valid.
