How To Identify Type I and Type II Errors In Statistics

This statistics video tutorial provides a basic introduction into Type I errors and Type II errors. A type I error occurs when a true null hypothesis is rejected. A type II error occurs when a false null hypothesis is not rejected. This video contains a few examples and practice problems of how to identify these errors.

Confidence Intervals & Margin of Error:

Find The Z-Score Given Confidence Interval:

Student's T-Distribution:

Hypothesis Testing - Null & Alternative:

Type I and Type II Errors:

One Tailed and Two Tailed Tests:

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Test Static For Means & Pop Proportions:

Hypothesis Testing Problems:

P-Value Method - Hypothesis Testing:

Hypothesis Testing - Proportions:

Difference of 2 Means - H Testing:

Cohen's D - Pooled Standard Deviation:

Hypothesis Testing - 2 Proportions:

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Matched or Paired Samples t-Test:

Chi Square Test:

Chi Square Distribution Test:

Test of Independence - Chi Square Dist:

Final Exams and Video Playlists:

Full-Length Videos and Worksheets:

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[eesaarashid]

So, when it comes to which type error has greater consequence it always depends on the context. In addition, as long as you justify it.
For examples for the last example I believe type 2 error is greater consequence as the defendant could be a murder. Therefore, a high risk chance of somebody being murdered. That's my opinion and you can argue with it the other way round

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[MubasherHassanKhanDhuddy]

Wonderful explanation. Identifying the bad decisions first and then see where NULL hypothesis is rejected, will lead to find TYPE I error. Wow. You nailed it. (Y)

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I really appreciate your simple approach to these examples. They really help to visualize the 2x2 grid.
However your wording for the 2nd example may throw people off.