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

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Statistical inference is a collection of procedures designed to allow us to make reliable conclusions from data collected from statistical samples to real or hypothetical statistical populations. It is the formal name of what we call interpreting statistical data.

The most common forms of statistical inference are:

1. point estimation
2. interval estimation
3. hypothesis testing
4. decision making

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. eclectic probability

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