The significance level (alpha or α ) is the probability of rejecting a correct null hypothesis. In effect, the level of significance gives complements the level of confidence (c) so that α = c-1. One must establish α before commencing on the actual data collection. The level of significance and sample size are crucial determinants of the approach that hypothesis takes.
A higher level of significance indicates that one may reject the null hypothesis. For example, a significance level of 0.15 shows a 15 percent risk of concluding the existence of a difference while in the real sense, it does not. Researchers use α to establish the most appropriate hypothesis in a study. Conversely, a lower significance level implies that the researcher must have substantial proof before rejecting the null hypothesis (Urbano, Lima & Hanjalic, 2019). Such evidence relies considerably on the sample of a given study, and more importantly, its size. For instance, a level of significance of 0.01 indicates that one is likely to encounter a 1 percent chance of digressing from the null hypothesis.
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Essentially, the sample size and significance level are interlinked to form an effective hypothesis testing. A bigger sample size eases the process of testing the research’s hypothesis. Also, such a size ensures that the hypothesis test is more sensitive and can thus reject a false null hypothesis. Ferguson (2016) associates smaller sample sizes to biased effects. It is worth contemplating that the probability of making an error while carrying out the test decreases with an increase in the sample size.
The choice of the sample size and significance level is a critical stage before experimenting. Researchers must choose appropriate sample sizes and estimate the significance level to ensure accurate conclusions derived from studies. Also, the decision is vital in minimizing future risks associated with efficiency and data reliability.
References
Ferguson, C. J. (2016). An effect size primer: a guide for clinicians and researchers.
Urbano, J., Lima, H., & Hanjalic, A. (2019). Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II, and Type III Errors. arXiv preprint arXiv:1905.11096 .
