What are Type I and Type II Errors (Corrected Version)

Oops! at 2:56, the "power of the test" is represented by "1-beta".

This is the corrected version to the previously uploaded file having observed some over-edited portions of the video. So, what are type I and type II errors? Type I error is the rejection of a true null hypothesis (also known as a "false positive" finding). Features are: Rejecting the true null hypothesis when it is true; represented by  (size of the statistical test); the probability of rejecting the true null hypothesis; a “false-positive” scenario; classical statistics generally concentrates on a Type I error. Example of a Type I Error: When a person who has committed a crime is given a “not guilty” verdict based on the facts presented before the court of law. “Not guilty” is not the same as saying “you are free”. The former statement is due to insufficient evidence to convict the accused. The Judge in this case has committed a Type I error by letting a criminal “off the hook!” Type II error is failure to reject a false null hypothesis (also known as a "false negative" finding). Features are: Failure to reject the null hypothesis when it is false; represented by 1-  (“power of the test”); the probability of accepting the false hypothesis; a “false-negative” scenario. Examples of a Type II Error: When an innocent man is convicted of a crime when he is in fact innocent. The Judge in this case has committed a Type II error by sending an innocent man to jail! In Medicine, telling patients that Drug B is no more harmful than Drug A when it actually is can have serious consequences. So, where is the trade-off? To minimise both errors a trade-off is required, but for any given sample size, it is not possible to minimise both at the same time. Keep the probability of committing a Type I error at a fairly low level, say at 0.01 or 0.05 (anything higher may lead to Type I error). Also, minimise the probability of having a Type II error as much as possible. What are the decision consequences? This is quite important for research involving medical and life sciences! Type I Error: Rejecting the true null hypothesis may have grave consequences. Type II Error: Failing to reject the null hypothesis when it is indeed false will have dire consequences (In my opinion, this is worse!). References used for this video tutorial are from: (1) Gujarati and Porter (2009) Basic Econometrics, 5ed and (2) Wooldridge, JM (2009) Introductory Econometrics: A Modern Approach, 4ed.

Follow up with soft-notes and updates from CrunchEconometrix:
Website: http://cruncheconometrix.com.ng
Blog: https://cruncheconometrix.blogspot.co...
Forum: http://cruncheconometrix.com.ng/blog/...
Facebook:   / cruncheconometrix  
YouTube Custom URL:    / cruncheconometrix  
Stata Videos Playlist:    • (Stata13):Estimate and Interpret Two-...  
EViews Videos Playlist:    • (EViews10):Interpret VECM, Forecast E...