frequencistas

Frequencistas, or frequencists, is a term used in Spanish- and Portuguese-language contexts to denote adherents of the frequentist approach in statistics. The label is not universally standardized and its exact usage can vary, but it generally refers to those who define probability as the long-run relative frequency of events.

In the frequentist paradigm, inference centers on sampling distributions and long-run error properties of procedures. Probabilities are attached to data and to the performance of statistical methods under repeated sampling, rather than to parameters or hypotheses. Common tools include hypothesis testing with p-values, construction of confidence intervals, and procedures designed to control error rates (such as alpha levels and power considerations). The emphasis is often on fixed, pre-specified rules for decision making, rather than on updating beliefs in light of prior information.

Historically, the frequentist school is associated with figures such as Ronald Fisher, Jerzy Neyman, and Egon Pearson, who developed foundational concepts like the Neyman–Pearson lemma, null hypothesis significance testing, and confidence intervals. The approach has been dominant in many scientific fields for much of the 20th century and remains widely used today. It has also faced criticism, including debates about the interpretation of p-values, the meaning of confidence intervals, and the impact of multiple testing or optional stopping. In modern practice, frequentist methods are often complemented by Bayesian or hybrid approaches, and techniques such as bootstrap and permutation tests have broadened the toolbox while addressing some traditional concerns.

Outside statistics, the term frequencista is rarely standardized and may be used inconsistently; when it appears, it most often denotes allegiance to frequency-based reasoning within statistical inference.