Ensuring a credible literature base is essential for all research fields. One element of this relates to the replicability of published work, which is the probability that the results of an original study would replicate in an independent investigation. A critical feature of replicable research is that the sample size of a study is sufficient to minimize statistical error and detect effects that exist in reality. A recent study (Buckley, Hyland, et al., 2023) estimated that the replicability of all quantitative technology education research is approximately 55% with this estimate showing an increasing trend in recent years. Given this estimate, it would be useful to invest efforts to improve replicability and thus credibility in the literature base in this way. Power analyses can be conducted when planning a quantitative study to support the determination of sample size requirements to detect population effects, however their existence in technology education research is rare. As the conduction of power analyses is a growing phenomenon in social scientific research more broadly, it is likely that one reason for their limited use by quantitative technology education researchers is a lack of resources within the field. As such, this article offers a primer for technology education researchers for conducting power analysis for common research designs within the field.