Difference Between Type I and Type II Error Discussion and Responses

These post replies need to be substantial and constructive in nature. They should add to the content of the post and evaluate/analyze that post answer. including one scholarly peer-reviewed reference. Minimum 100 words. A type I error occurs when a researcher rejects the null hypothesis despite being correct. Or, to put it another way, type I errors are conclusions drawn when a treatment has no effect but is effective. Examples of this include studies on the effectiveness of medications for reducing hypertension. For example, a patient’s hypertension levels may appear to fall after treatment even while it isn’t working(Lu et al., 2019). The probability of type 1 error is fixed in advance. Suppose we choose α = 0.05, or 5% significance level. It implies that there are about 5 chances in 100 to commit type 1 error, that is rejecting H0 when H0 is true. We can control type 1 error by fixing it at a lower level. But we cannot control type 2 error (Chiang et al., 2016). For a fixed sample size n, if we reduce type 1 error, the probability of committing type 2 error increases. We cannot control both errors simultaneously.

There is a trade-off between these two types of errors. Although type I and type II errors can never be avoided entirely, the investigator can reduce their likelihood by increasing the sample size (gohary, 2019) If type 1 error is increased, it will lead the researcher to reject the null hypothesis even when it is actually true. On the other hand, if we reduce type 1 error to a minimum, there is a possibility of increasing type 2 error, which will lead the researcher to accept the null hypothesis even when it is actually false (Thompson, 2011). One way to avoid or minimize type 1 and type 2 errors is to abandon significance testing. Instead of significance testing, we can use confidence interval. We can then reduce both type 1 and type 2 error to zero (Rothman, 2010). References Chiang, K. S., Bock, C. H., El Jarroudi, M., Delfosse, P., Lee, I. H., & Liu, H. I. (2016). Effects of rater bias and assessment method on disease severity estimation with regard to hypothesis testing. Plant Pathology, 65(4), 523–535. https://doi.org/10.1111/ppa.12435 gohary, T. (2019). Hypothesis testing, type I and type II errors: Expert discussion with didactic clinical scenarios. International Journal of Health and Rehabilitation Sciences (IJHRS), 8(3), 132. https://doi.org/10.5455/ijhrs.0000000180 Lu, J., Qiu, Y., & Deng, A. (2019). A note on Type S/M errors in hypothesis testing. British Journal of Mathematical and Statistical Psychology, 72(1), 1–17. https://doi.org/10.1111/bmsp.12132 Rothman, K. J. (2010). Curbing type i and type II errors. European Journal of Epidemiology, 25(4), 223–224.

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https://doi.org/10.1007/s10654-010-9437-5 Thompson, S. (2011). On the Distribution of Type II Errors in Hypothesis Testing. Applied Mathematics, 02(02), 189–195. https://doi.org/10.4236/am.2011.22021 These post replies need to be substantial and constructive in nature. They should add to the content of the post and evaluate/analyze that post answer. including one scholarly peer-reviewed reference. Minimum 100 words. Hypothesis testing is the sheet anchor of empirical research and in the rapidly emerging practice of evidence-based medicine. However, empirical research and, ipso facto, hypothesis testing have their limits. The empirical approach to research cannot eliminate uncertainty completely. At the best, it can quantify uncertainty. This uncertainty can be of 2 types: Type I error (falsely rejecting a null hypothesis) and type II error (falsely accepting a null hypothesis). The acceptable magnitudes of type I and type II errors are set in advance and are important for sample size calculations. Another important point to remember is that we cannot ‘prove’ or ‘disprove’ anything by hypothesis testing and statistical tests. We can only knock down or reject the null hypothesis and by default accept the alternative hypothesis. If we fail to reject the null hypothesis, we accept it by default (Banerjee et al., 2009) In statistic, type I error is a false positive conclusion, while a type II error is a false negative conclusion. Making a statistical decision always involves uncertainties, so the risks of making these errors are unavoidable in hypothesis testing. The probability of making a Type I error is the significance level, or alpha (α), while the probability of making a Type II error is beta (β).

These risks can be minimized through careful planning in your study design (Type I and Type II Errors, 2021) Researchers should be aware of the likelihood of a type II error. The greater the N within a study, the more likely it is that a researcher will reject the null hypothesis. The concern with this approach is that a very large sample could show a statistically significant finding due to the ability to detect small differences in the dataset; thus, utilization of p values alone based on a large sample can be troublesome (Shreffler & Huecker, 2022) Reference Banerjee, A., Chitnis, U. B., Jadhav, S. L., Bhawalkar, J. S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Industrial Psychiatry Journal, 18(2), 127–131. https://doi.org/10.4103/0972-6748.62274 Shreffler, J., & Huecker, M. R. (2022). Type I and Type II Errors and Statistical Power. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK557530/ Type I and Type II errors. (2021, January 18). Scribbr. https://www.scribbr.com/statistics/type-i-and-type-ii-errors/ Question Discuss the hypothesis testing issues of Type I and Type II errors and how those can cause the researcher to reach a wrong conclusion about rejecting the null hypothesis. What steps must researchers take to avoid Type I and Type II errors? Be sure to support your statements with logic and argument, use at least two peerreviewed articles and cite them to support your statements.

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Difference Between Type I and Type II Error Discussion and Responses