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When researchers are testing their hypotheses, the Alpha level should show significant results of your experiment. The researcher’s experiment should have enough evidence to support their proposed theory. When planning their experiment, they can set their Alpha level to what they want, and usual value is 5%, or 5 in 100 probability that the result is achieved by accident.
There are specific instances where it is more reliable to use a lower alpha level compared to a higher alpha. Your findings will have more significance and rate of accuracy, the lower the alpha. Alpha levels are set low to help decrease the possibility of obtaining Type 1 error. For example, if there’s a possibility of serious injury or death, we want to make the level smaller. When you have a high alpha level, the chance of having an error is raised. There are much stricter alpha levels when pharmaceutical companies are conducting research on new medications.
If the alpha levels are poorly designed, the results will not be accurate and have little importance in the research. A larger value of alpha, even one greater than 0.10 may be appropriate when a smaller value of alpha results in a less desirable outcome. Type 1 errors can occur when concluding your results. An example of when a higher level is acceptable is when the research has no significance on death or injury and can prove the theory that is being conducted.
The Minitab Blog, (2012). Alphas, P-Values, and Confidence Intervals, Oh My! Retrieved from http://blog.minitab.com/blog/alphas-p-values-confi… October 9, 2018
Grove, S.K. & Cipher, D. (2017). Statistics for Nursing Research: A workbook for evidenced based practice. https://pageburstls.elsevier.com/#/books/978032335… October 9, 2018