- The Formal Hypothesis Test.

- The Meaning of Statistical Significance Levels.

This will either verify or reject the null hypothesis. Significance levels are bound up with the null set. Null hypotheses may actually be true even if they are rejected by the data. This gives rise to a Type I error. Failing to reject a false null hypothesis creates a Type II error.

The probability of a Type I error is the level of significance. This, as noted above, must be set before data is collected. The higher the level of significance, the greater the weight of statistical evidence needed to reject the null hypothesis, avoiding the Type I error as above. The significance levels can effectively control the chances of an error occurring.

- Effects of Missing or Mixing Steps.

Assigning a significance level after performing the test means that all data objectivity is lost: The results can be made to mean anything you want them to be. This is far from scientific, and will give a skewed, subjective result.