The response to this question to a considerable extent can be subjective since it is a judgment call. Every alpha level is reliant on the circumstances that relate to specific research. For instance, research on cancer focusing on whether some of the cancerous body organs should be eliminated is likely to set up a rigid alpha at 0.01 (Banerjee et al., 2009). It would be inhumane and unreasonable to eradicate a cancer-free organ, and nobody would want that to happen. On contrary, if the alpha is so rigid and find out that the organ is cancer-free, while in reality it is infected, the patient is likely to die due too soon due to the type II error made, failure to detect the variance when the infection is there (Banerjee et al., 2009). From the above incidence, it is evident that this is a highly frail balance.
A scenario where alpha can increase to 0.1 can differ significantly. One of the situations that alpha can rise is whereby the study is not that important (Banerjee et al., 2009). A good example of such study is mood therapy focused on examining whether it has an impact. In such a situation I may increase alpha although this increases the likelihood of committing type I error. In many biased types of research, alpha is raised up to 0.25 to try to achieve pleasant results. However, this raises chances of type I error by about 25% (Banerjee et al., 2009). For this reason, it is vital to understand statistics to make conclusions that are essential.
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One of the major reason why one can raise type I error rate is a situation where the sample size is too small or limited (such that using the alpha of 0.05 may not create or achieve any impact. Another reason is a situation where the resources injected into research is not enough to research at 0.05 level.
References
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.
