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Just curious as to what the answer is!

user199010
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Why don’t you need hypothesis testing when we’re computing descriptive statistics?

Because you are simply describing your sample without making any inferences to the population from which the sample was thought to be taken (this would be called inferential statistics for which you would formulate hypotheses and test them).

The description of the sample, as in descriptive statistics, can include the mean, median and mode (measures of central tendency), the standard deviation (dispersion), the minimum and maximum values (the range of the data), as well as the shape of the data distributions such as skewness and kurtosis.

This post might be of interest too: When do we use S.E.M. and when do we use S.D.?

Stefan
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You CAN use hypothesis testing with "descriptive statistics," you just need to define a hypothesis.

If you are just looking for a description of what a dataset actually is, you aren't making hypotheses, you're making observations and describing them.

If you wanted to compare what your dataset may represent (as in your dataset is a subset of a larger population, for example), then you could present things like confidence intervals that would show how likely your dataset is to represent the population. Then your question would be "are the data I see similar to the population data as a whole?" and the hypothesis to test would be "these data are similar to the population as a whole," which you could test using the various hypothesis tests.

Hope that answers your question!

stensy
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    This makes a powerful implicit assumption that the dataset represents anything besides itself. Not all datasets are samples. – whuber Mar 15 '18 at 18:55
  • Excellent point! I work primarily with medical data where almost all the time we have samples, but I should be careful to be specific. – stensy Mar 16 '18 at 00:00