Precision and Accuracy are two concepts which seem to get confused frequently. Estimates can be precise without being accurate, a fact often glossed over when interpreting computer output containing results specified to the fourth or sixth or eighth decimal place. Assessing causality is the reason of most statistical analysis, yet its subtleties escape many statistical consumers. That is, the experimenter must be the one assigning values of predictor variables to cases. If the values are not assigned or manipulated, the most one can hope for is to show evidence of a relationship of some kind.
Observational studies are very limited in their ability to illuminate causal relationships.
In this paper, some of the trickier aspects of applied data analysis have been discussed. The right amount of power should be included. The best available measurement tools should be used. If there are errors in the measures, that fact must be taken into account when interpreting the results. Multiple comparisons need to be watched closely.