Explain why measures of variability are essential to inferential statistics.
When looking at inferential stats, we are trying to see if we can make inferences (conclusions or analysis) on a set of data. We can't just eye ball the data, we need to actually sit down and compute statistics to make a statistically sound conclusion on the data. This is what distinguishes inferential stats from descriptive stats that just looks at the data from a superficial point of view.
What type of tests do we do for inferential stats? The two main are estimation and hypothesis testing.
This posting begins by defining what inferential statistics are, and then explains what 2 of the most popular inferential statistics are.