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Type 1 error8/5/2023 To account for this, statistical adjustments can be made to correct for multiple comparisons. While an understanding of these two scenarios is necessary for all researchers undertaking statistical analysis, the nature of neuroimaging analyses and the volume of statistical comparisons conducted during many types of neuroimaging analyses mean that these errors are more likely to occur than within other fields 148,149 (Lindquist and Mejia, 2015 Hupé, 2015). The more statistical comparisons performed in a given analysis, the more likely a Type I or Type II error is to occur. A Type II error is the acceptance of the null hypothesis when a true effect is present (a false negative). A Type I error refers to the incorrect rejection of a true null hypothesis (a false positive). The results of statistical analyses are susceptible to both Type I and Type II errors. Johnson, Sarah Gregory, in Progress in Molecular Biology and Translational Science, 2019 3.7.3 Correction for multiple comparisons The answer to this question depends on the purpose of the research as well as the potential implications in the presence of a false positive (type I error) or false negative (type II error) findings.Įileanoir B. Essentially, the investigator is confronted with the question of what type of error is more costly. Ultimately, the scientist must decide which type of error is more problematic to his or her research. If a type II error has been committed and that particular line of inquiry is not pursued further, the scientific community may miss valuable information. Unfortunately, in the presence of a type II error, the line of inquiry is often discarded, because in most fields of research, a premium is placed on statistically significant results. In other words, scientific research is cumulative therefore, false positives are revealed in subsequent studies. Typically, once statistical relationships are discovered, more studies follow that confirm, build upon, or challenge the original findings. The major drawback to exclusively emphasizing type I error over type II error is simply overlooking interesting findings. Researchers are generally adverse to committing this type of error consequently, they tend to take a conservative approach, preferring to err on the side of committing a type II error. In the presence of a type I error, statistical significance becomes attributed to findings when in reality no effect exists. Type I error has historically been the primary concern for researchers. Although a researcher can take several measures to lower type I error, or alternatively, a type II error, empirical research always contains an element of uncertainty, which means that neither type of error can be completely avoided. Unfortunately, there is not a cure-all solution for preventing either error moreover, reducing the probability of one of the errors increases the probability of committing the other type of error. Type I and type II errors present unique problems to a researcher. Doan, in Encyclopedia of Social Measurement, 2005 Conclusion: The Trade-off between the Two Errors
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