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## Summary of Type I and type II errors

Type I and type II errors

An alpha value measures the amount of risk in interpreting results of an experiment and is equal to the risk of obtaining false positive results called Type I errors. False negative results, called Type II errors, are measured by a beta value. In order to reduce the chances of obtaining a false negative or a false positive result, the sample size must be increased. A power value measures the likelihood that statistically non-significant results are actually non-significant, rather than a Type II error. Effect size is used in determining the significance of results. In order to reduce chances of obtaining a false negative or false positive result, or to detect a smaller effect or accommodate for larger standard deviations, a larger sample size must be evaluated.

## Flashcards on Type I and type II errors

### Type I and type II errors

10 flashcards

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The only way to reduce the chance of both type I and type II errors is to increase the .

## Questions on Type I and type II errors

### Type I and type II errors

USMLE® Step 1 style questions

3 questions

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A student researcher has gathered data from a study to evaluate treatment options for weight loss in children with overweight or obesity. The study was a non-randomized and non-blinded family based intervention. The main outcome of interest is improvement in quality of life scores as measured by a subjective scoring system completed by both the parent and child. The data from the study are presented below including the average score and the standard deviation (SD). When considering statistical testing, an alpha level of 0.05 is chosen. Which of the following best explains the implications of this alpha level?

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