Paragraph 5

Paragraph 5

Please write a Paragraph answering to this discussion below with your opinion. Please include citations and references in alphabetical order in case of another source.

There are different instances where it is more acceptable to use a lower alpha level compared to a higher alpha level. A more significant value of alpha, even one greater than 0.10 may be appropriate when a smaller value of alpha results in a less desirable outcome. Having a poorly designed alpha level can leave your results without any importance or meaning. Errors can also happen when concluding your results known as Type One errors. The alpha level is significant to the results of your experiment. In other words, your analysis has shown sufficient evidence to support your proposed theory each alpha level is dependent on the circumstances that surround a particular study. For example, if I were doing a test for cancer and that test would determine if I should remove some cancerous organ then I would want to set the alpha level very stringent at 0.01. I
certainly do not want to remove an organ if it is cancer free. On the other hand, if I set the alpha level too stringent and I determine that the organ does not have the disease, when in fact it does then the patient may die prematurely because I made a type II error, failure to detect a difference when one, in fact, does exist. As you can see this is a delicate balance Situations, where you might raise the alpha to 0.1, can vary greatly. The obvious one that many of you mentioned is when the results of the study are not that critical. For example, I am trying to see if a mood therapy effects, I might raise my alpha to find my impact even though I increase the chance of making a type I error. At the beginning of the class when a number of you mentioned that certain studies are biased because the results didn’t seem right, well they could have raised their alpha to 0.25 to say their product is better, and the statistics will support that claim. However, the chance of making a type I error is 25%! This is why it is essential to know statistics to make decisions that are important to you. Generally, the only reason that you would raise your type I error rate is if you have a limited sample size (too small a sample and you will not find your effect at the traditional 0.05) or if you do not have the money to conduct the study at the 0.05 level (this usually only happens in the private sector, published research is generally not accepted if it is at 0.1 especially if an adequate sample size was feasible).