Final answer:
The claim that non-normality is more concerning than heteroscedasticity is false. Which of the two is of greater concern depends on the context of the statistical analysis. In many cases, heteroscedasticity can affect the efficiency of estimates and the reliability of standard errors more significantly than non-normality. Therefore , the correct answer options is b)
Step-by-step explanation:
The statement that "non-normality is generally more concerning than heteroscedasticity" is false. The concern for non-normality versus heteroscedasticity is context dependent. In regression analysis, for example, if the sample size is large, the Central Limit Theorem suggests that the sampling distribution of the mean is approximately normal even if the data itself is not normally distributed.
This occasionally makes non-normality less of a concern. However, heteroscedasticity can lead to inefficient estimates and may affect the standard errors of the regression coefficients, possibly leading to incorrect conclusions about hypothesis tests.
Therefore, whether non-normality or heteroscedasticity is more concerning depends on the specific statistical analysis you are performing. It's important to analyze both and take corrective measures appropriate for the data and the analysis being done.