Basic Statistics and Data Analysis

Lecture notes, MCQS of Statistics

Type I error, Type II error and minimizing the risk of both of these types of errors

Type I and Type II Errors

In hypothesis testing there are two possible errors we can make: Type I and Type II errors.

  • A Type I error occurs when your reject a true null hypothesis (remember that when the null hypothesis is true you hope to retain it).
    α=P(type I error)=P(Rejecting the null hypothesis when it is true)
    Type I error is more serious than type II error and therefore more important to avoid that a type II error.
  • A Type II error occurs when you fail to reject a false null hypothesis (remember that when the null hypothesis is false you hope to reject it).
    β=P(type II error) = P(accepting null hypothesis when alternative hypothesis is true)
  • The best way to allow yourself to set a low alpha level (i.e., to have a small chance of making a Type I error) and to have a good chance of rejecting the null when it is false (i.e., to have a small chance of making a Type II error) is to increase the sample size.
  • The key in hypothesis testing is to use a large sample in your research study rather than a small sample!

If you do reject your null hypothesis, then it is also essential that you determine whether the size of the relationship is practically significant.
The hypothesis test procedure is therefore adjusted so that there is a guaranteed “low” probability of rejecting the null hypothesis wrongly; this probability is never zero.

The Author

Muhammad Imdadullah

Student and Instructor of Statistics and business mathematics. Currently Ph.D. Scholar (Statistics), Bahauddin Zakariya University Multan. Like Applied Statistics and Mathematics and Statistical Computing. Statistical and Mathematical software used are: SAS, STATA, GRETL, EVIEWS, R, SPSS, VBA in MS-Excel. Like to use type-setting LaTeX for composing Articles, thesis etc.

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