In this paper (Rubin, 2021), I consider two types of Type I error probability. The Neyman-Pearson Type I error rate refers to the maximum frequency of incorrectly rejecting a null hypothesis if a test was to be repeatedly reconducted on a series of different random samples that are all drawn from the exact same null population. Hence, the Neyman-Pearson Type I error rate refers to a long run of exact replications. In contrast, the Fisherian Type I error probability is the probability of incorrectly rejecting a null hypothesis in relation to a hypothetical population that reflects the relevant characteristics of the particular sample under consideration. Hence, the Fisherian Type I error rate refers to a one-off sample rather than a series of samples that are drawn during a long run of exact replications.
I argue that social science deals with units of analysis (people, social groups, and social systems) that change over time. As the Greek philosopher Heraclitus put it: “No man ever steps in the same river twice, for it’s not the same river and he’s not the same man.” Rivers and men are what Schmidt (2009, p. 92) called “irreversible units” in that they are complex time-sensitive systems that accumulate history. The scientific investigation of these irreversible units cannot proceed on the assumption that exact replications are possible. Consequently, the Neyman-Pearson Type I error rate is irrelevant in social science, because it relies on a concept of exact replication that cannot take place in the case of people, social groups, and social systems. Why should social scientists be interested in an error rate for an impossible process of resampling from the same fixed and unchanging population?
I argue that the Fisherian Type I error probability is more appropriate in social science because it refers to one-off samples from hypothetical populations. In this case, researchers recognise that every sample comes from a potentially different population. Hence, researchers can apply the Fisherian Type I error probability to each sample-specific provisional decision that they make about rejecting the same substantive null hypothesis in a series of direct replications.
I conclude that the replication crisis may be partly (not wholly) due to researchers’ unrealistic expectations about replicability based on their consideration of the Neyman-Pearson Type I error rate across a long run of exact replications.
For further information, please see:
Rubin, M. (2021). What type of Type I error? Contrasting the Neyman-Pearson and Fisherian approaches in the context of exact and direct replications. Synthese, 198, 5809–5834. . https://doi.org/10.1007/s11229-019-02433-0 *Self-archived version*
I also discuss the differences between the Fisherian and Neyman-Pearson approaches to hypothesis testing here.