The topics below are usually included in the area of interpreting statistical data. A more formal name for this topic is statistical inference.

1. Statistical assumptions
2. Likelihood principle
3. Estimating parameters
4. Testing statistical hypotheses
5. Revising opinions in statistics

planning statistical research -- summarizing statistical data

Statistical inference is inference about a population from a random sample drawn from it or, more generally, about a random process from its observed behavior during a finite period of time. It includes:

1. point estimation
2. interval estimation
3. hypothesis testing (or statistical significance testing)
4. prediction

There are several distinct schools of thought about the justification of statistical inference. All are based on some idea of what real world phenomena can be reasonably modeled as probability.

1. frequency probability
2. personal probability
3. Bayesian probability
4. eclectic probability