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# Type I Error – Definition

### Type I Error Definition

A type I error is a type of error that occurs in statistical hypothesis testing in which a true null hypothesis is rejected. When a null hypothesis which is actually true is rejected when it should not be, a type I error has occurred. In statistical hypothesis testing, a null hypothesis is often established before the test. A type I is also called a ‘false positive’ finding or conclusion, it is an error of rejecting a true null hypothesis that should otherwise not be rejected. This error thereby accepts an alternative hypothesis when the results of the test are still subject to probability.

### A Little More on What is Type I Error

A type I error often occurs when there is an absence of a relationship between the item being tested, the stimuli applied and the outcome. A Type I error is called a ‘false positive’ finding or conclusion that occurs when the outcome of a hypothesis test is caused by something outside of the stimuli or caused by chance. When there is no connection between the tets subject and the stimuli, a false positive is given. When this happens, there is a rejection of a null hypothesis which is actually true and meant to be accepted.

### Example of a Type I Error

A type I error can occur in diverse fields and various testings, for instance, if a new drug is being tested in a medical laboratory, a type I error when the drug is taken to be an effective remedy for a particular illness when it is actually not. In the legal field, a type I error occurs when an innocent person is sent to jail when he should not have been. One common attribute of type I errors is that something which should not have been rejected is rejected or something which should not have been accepted is accepted.

### References for “Type I Error”

https://en.wikipedia.org/wiki/Type_I_and_type_II_errors

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### Academic research for “Type I Error”

Type I and Type II error concerns in fMRI research: re-balancing the scale, Lieberman, M. D., & Cunningham, W. A. (2009). Type I and Type II error concerns in fMRI research: re-balancing the scale. Social cognitive and affective neuroscience, 4(4), 423-428.

Evaluating type I error and power rates using an effect size measure with the logistic regression procedure for DIF detection, Jodoin, M. G., & Gierl, M. J. (2001). Evaluating type I error and power rates using an effect size measure with the logistic regression procedure for DIF detection. Applied measurement in education, 14(4), 329-349.

Group sequential designs using a family of type I error probability spending functions, Hwang, I. K., Shih, W. J., & De Cani, J. S. (1990). Group sequential designs using a family of type I error probability spending functions. Statistics in medicine, 9(12), 1439-1445.

Design and analysis of group sequential tests based on the type I error spending rate function, Kim, K., & Demets, D. L. (1987). Design and analysis of group sequential tests based on the type I error spending rate function. Biometrika, 74(1), 149-154.

Simulation studies of the effects of small sample size and studied item parameters on SIBTEST and Mantel‐Haenszel Type I error performance, Roussos, L. A., & Stout, W. F. (1996). Simulation studies of the effects of small sample size and studied item parameters on SIBTEST and MantelHaenszel Type I error performance. Journal of Educational Measurement, 33(2), 215-230.

Type I error rate comparisons of post hoc procedures for I j Chi-Square tables, MacDonald, P. L., & Gardner, R. C. (2000). Type I error rate comparisons of post hoc procedures for I j Chi-Square tables. Educational and psychological measurement, 60(5), 735-754.

Comparison of type I error rates for statistical analyses of resource selection, Bingham, R. L., & Brennan, L. A. (2004). Comparison of type I error rates for statistical analyses of resource selection. The Journal of Wildlife Management, 68(1), 206-212.

Type I error and the number of iterations in Monte Carlo studies of robustness, Robey, R. R., & Barcikowski, R. S. (1992). Type I error and the number of iterations in Monte Carlo studies of robustness. British Journal of Mathematical and Statistical Psychology, 45(2), 283-288.

Multiple testing. Part I. Single-step procedures for control of general type I error rates, Dudoit, S., van der Laan, M. J., & Pollard, K. S. (2004). Multiple testing. Part I. Single-step procedures for control of general type I error rates. Statistical Applications in Genetics and Molecular Biology, 3(1), 1-69.