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Power: The probability that you would identify a true effect of the independent variable.

Learn the significance of Type Two errors in statistical analysis, including causes and how to mitigate them to ensure accurate results. Discover how power relates to identifying true effects of independent variables and avoiding misleading conclusions.

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Power: The probability that you would identify a true effect of the independent variable.

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  1. Power: The probability that you would identify a true effect of the independent variable.

  2. Type Two error: Labeling an outcome as related to random events when it actually is because of the independent variable.

  3. Type Two error: Accepting the null hypothesis when it is false.

  4.  error Where  represents the probability that this type of error would occur.

  5. Ho :  = 50 H1:  > 50  = .05 50 o

  6. 0 1  

  7. Power = 1 -  0 1  

  8. Three things can effect power. 1) The level of significance

  9.  gets smaller  gets bigger

  10. 2) Increase the sample size (N)

  11. Larger N - smaller standard error Therefore less overlap of the distributions

  12. 3) Increase the distance from 0 to  1

  13. 0 1  

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