Statistical Significance

Authors
Affiliations

Doctor of Physical Therapy

B.S. in Kinesiology

Doctor of Physical Therapy

B.A. in Neuroscience

Understanding statistical significance requires an understanding of the interplay between 3 items:

  1. Decision Errors1
  2. Effect Size1
  3. Statistical Power1

Decision Errors

Understanding decision errors allows you to determine how confident you are that your conclusions are accurate1.

Effect Size

The p-value demonstrates if there is an effect1. Effect size tells you how big the effect is1.

Decision Errors

Decision errors refer to when the right procedures lead to wrong decisions/conclusions1. Decision errors in hypothesis testing include Type I Error (α) and Type II Error (β)1.

Type I Error

  • Occurs when the null hypothesis is true and you mistakenly reject it1.
  • α is the chance of making a Type I Error
    • p-value: 0.20 = 20% α
    • p-value: 0.05 = 5% α

Type II Error

  • Occurs when the research hypothesis is true, but not extreme enough to be considered statistically significant1.
  • β is the chance of committing a type II error1.

Effect Size

Example

Interactive Example

References

1.
Aron A, Coups EJ, Aron E. Statistics for Psychology. 6th ed. Pearson; 2013.

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