Question

Researchers routinely choose an alpha level of 0.05 for testing their hypotheses. What are some experiments for which you might want a lower alpha level (e.g., 0.01)? What are some situations in which you might accept a higher level?

Answer

In order to carry out an experiment, one has to have a hypothesis. A hypothesis test is therefore taken to prove whether the evidence provided is enough to accept one of the two hypotheses provided: the null or the alternate hypothesis. In every experiment, there is always a chance of committing Type I Error, whereby the null hypothesis is rejected when it should be accepted (Cohen, 2016). The alpha level, which in this case researchers routinely set at 0.05, is the probability of committing Type I Error. For this reason, it is important to know when to use a lower alpha level and when it is okay to use a higher alpha level, especially in nursing and medicine at large.

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For instance, when determining whether a certain treatment decreases mortality or whether a new trial drug decreases the susceptibility to a new disease, it would be advisable to use a lower alpha level, 0.01. In this case, using an alpha level of 0.01 simply means that the researcher is going to reject a null hypothesis when they should be accepting it 1 time out of 100 (Gaskin &Happell, 2014). The reasonable doubt would be considered acceptable and the hypothesis well-proven.

In contrast, there are times when a higher alpha level can be considered. This is especially so in situations that are not life-threatening (Grandell-Niemi et al., 2003). For example, a researcher who seeks to find out whether nurses at a healthcare facility have job satisfaction can use a higher alpha level. This is, however, only acceptable when the sample size is relatively large. This way, if one uses an alpha level of 0.1, it would mean that the probability or committing Type I Error is at 10%. Ultimately, the researcher has to know the level of accuracy required and seriousness of the experiment.

References

Cohen, J. (2016). Methodological issues and strategies in clinical research. American Psychological Association.

Gaskin, C. J., &Happell, B. (2014). Power, effects, confidence, and significance: An investigation of statistical practices in nursing research. International journal of nursing studies, 51(5), 795-806.

Grandell‐Niemi, H., Hupli, M., Leino‐Kilpi, H., &Puukka, P. (2003). Medication calculation skills of nurses in Finland. Journal of Clinical Nursing, 12(4), 519-528.