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Posted: December 16th, 2022
Reply to discussion on statistics
From the original discussion below : Can you highlight a scholarly article/research article and what their result was? What did their p-value provide as related to their hypothesis?
The alpha level is the probability of rejecting the null hypothesis when it is true. The beta level is the probability of accepting the null hypothesis when it is false. The alpha level is also known as type 1 error, which is rejecting a true null hypothesis while the beta level is also known as type 2 error, which is accepting a false hypothesis. The chosen alpha level is critical in deciding whether to reject or accept the hypothesis of a given study. For example, if the p-value is 0.07, and the alpha level is 0.05, then the null hypothesis is accepted. Also, if the p-value is 0.07, and the alpha level is 0.10, then the null hypothesis is rejected.
Changing the alpha or beta level of a study can lead the study to be showing errors of accepting a hypothesis when it should be rejected (Pagano, 2012). The probability of rejecting the null should be lowered to avoid error from both values. The alpha level is usually set at 0.05 but can range from 0.01 to 0.10 while beta can be set from 0.05 to 0.20 (Pagano, 2012). A smaller alpha level affects the statistical significance of a study result by assisting to show more precision of a larger population. Statistical significance indicates whether or not if the result of a given experiment meets an acceptable level of significance to ensure the statistics are correct (Frick et al.,2011). In addition, statistical significance is the likelihood that a relationship between two or more variables is caused by something other than chance. On the other hand, clinical significance refers to the magnitude of the actual treatment effect that will determine whether the results of the study are likely to have an impact on recent medical practice (Frick et al.,2011).
References
Frick, K.D., Milligan, R.A., and Pugh, L.C. (2011). Calculating and interpreting the odds ratio. American Nurse Today, 6(3). Retrieved from https://americannursetoday.com/calculating-and-interpreting-the-odds-ratio/
Pagano, R. R. (2012). Understanding statistics in the behavioral sciences. Cengage Learning.
250 words, APA format, scholarly article/resource required.
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