What are some descriptive and non-descriptive methods of data analysis that can be used with a data set of both categorical and numeric data?

There are various methods of analyzing a dataset which includes the descriptive or the non-descriptive methods of analysis.

The descriptive methods help us get a summary of the data at hand presents in such a way that meaningful points can be taken into consideration. The descriptive methods include the measures of central tendency, that are the mean, median, and mode besides the measures of the spread of the data given by the variance. Certain other factors like the skewness and kurtosis also help us to find the distribution of the dataset and the curve of the given data so that any patterns within the date can be considered and appropriate actions could be taken.

Now, this is only the descriptive side that helps us know the values of the parameters but does not let us conclude their significance which is yet another important part. This can be done by using the non-descriptive methods, that is, our inferential statistics.

Inferential statistics involve decision making and the level of significance of the particular parameter. It involves various statistical tests ranging from parametric ones to the non-parametric ones. The former is used when the distribution of the data is known and the scale of measurement of the dataset is in interval or ratio scale. On the other hand, when the data is of a nominal or ordinal scale, the non-parametric methods may be used if considered upon a broad level. Besides this, various other factors help us decide which test (parametric or non-parametric) is ought to be used.

The statistical tests involved in inferential, statistics include Z-test, t-test. F-test and so on whereas the non-parametric test includes the Kolmogorov-Smirnov Test, Kruskal-Wallis Test, Wilcoxon matched-pair signed-rank tests, etc.