When we talk about skewness and kurtosis, we are looking at some important features of data. These features can help us choose the right statistical tests.

Skewness is about how much a data set leans to one side. If a data set is positively skewed, it means it has a longer tail on the right side. In this case, regular tests like the t-test might not give good results. Instead, we could use a different test, called the Mann-Whitney U test, which is better for this kind of data.

Kurtosis looks at how heavy the tails of a data set are. A high kurtosis means there are more extreme values, called outliers. If your data has high kurtosis, using methods that are affected by these outliers, like the average (or mean), could lead to wrong conclusions. In this situation, it's better to use methods that rely on the median, which is less sensitive to those outliers.

So, to sum it up, checking skewness and kurtosis helps you make smart choices about which statistical tests to use. This way, you can get results that are reliable and fit well with your data's features.