Common Errors in Statistics (and How to Avoid Them), 4th Edition

PRESCRIPTION

Statistical methods used for experimental design and analysis should be viewed in their rightful role as merely a part, albeit an essential part, of the decision-making procedure.

Here is a partial prescription for the error-free application of statistics.

1. Set forth your objectives and your research intentions before you conduct a laboratory experiment, a clinical trial, or survey, or analyze an existing set of data.

2. Define the population about which you will make inferences from the data you gather.

3. 

a) Recognize that the phenomena you are investigating may have stochastic or chaotic components.

b) List all possible sources of variation. Control them or measure them to avoid their being confounded with relationships among those items that are of primary interest.

4. Formulate your hypotheses and all of the associated alternatives. (See Chapter 2.) List possible experimental findings along with the conclusions you would draw and the actions you would take if this or another result should prove to be the case. Do all of these things before you complete a single data collection form, and before you turn on your computer.

5. Describe in detail how you intend to draw a representative sample from the population. (See Chapter 3.)

6. Use estimators that are impartial, consistent, efficient, robust, and minimum loss. (See Chapter 5.) To improve results, focus on sufficient statistics, pivotal statistics, and admissible statistics, and use interval estimates. ...