One suggested approach to reduce skew in data prior to testing is which of the following?

One suggested approach to reduce skew in data prior to testing is which of the following?

• A. Transform the data
• B. Increase sample size
• C. Use a higher alpha
• D. Use more variables

Answer: (A)

When data are skewed, transforming the data is a common way to make the distribution more symmetric and to stabilize variance before applying statistical tests that assume normality and homoscedasticity. Transformations like logarithmic, square root, or Box-Cox can reduce positive skew and bring the data closer to a normal shape, which makes parametric tests more valid and improves the reliability of p-values and confidence intervals. After transforming, you analyze on the transformed scale (and you can back-transform for interpretation if needed). Increasing sample size doesn’t fix skewness and mainly affects precision, not the distribution’s shape; using a higher alpha increases the chance of false positives; using more variables doesn’t address skewness and can introduce other issues.

When data are skewed, transforming the data is a common way to make the distribution more symmetric and to stabilize variance before applying statistical tests that assume normality and homoscedasticity. Transformations like logarithmic, square root, or Box-Cox can reduce positive skew and bring the data closer to a normal shape, which makes parametric tests more valid and improves the reliability of p-values and confidence intervals. After transforming, you analyze on the transformed scale (and you can back-transform for interpretation if needed). Increasing sample size doesn’t fix skewness and mainly affects precision, not the distribution’s shape; using a higher alpha increases the chance of false positives; using more variables doesn’t address skewness and can introduce other issues.