Explains experimental biases that should be avoided.

Explains why the type of error matters in statistics.

How degrees of freedom and effect size determine the usefulness of a statistical study.

Provides real life examples of how biased algorithms in big data can have serious effects; for example, how the "Compas" algorithm is used by courts to determine recidivism rates despite the possibility that the algorithm is racially discriminatory.

How sample size can be used to increase statistical power.