To make data and metrics useful, you need to be able to draw inferences. Statistics can help you interpret your data and determine these conclusions. From measures of averages to t-tests to regression analysis, understanding statistics is critical. Brush up on your stats knowledge with lots of free materials in the Khan Academy's math and probability subject area.

Statistical analysis can be powerful, but is not without its pitfalls. Alex Reinhart, a statistics Ph.D. student, highlights some popular missteps (even among scientists) in Statistics Done Wrong. Below are two common pitfalls to keep in mind:

Correlation doesn’t equal causation.

Two variables might be correlated (i.e., they move in consistent directions in relation to each other) but that doesn’t mean that one causes the other. The blog Spurious Correlations showcases some examples.

Regression to the mean.

This statistical concept explains why very tall parents tend to have shorter children, as phenomena tend to return to average over time. What might look like improvement (e.g., a low performer transfers to a new team and improves) or even decline (e.g., a star team gets worse over time) is just the tendency to return to average.