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	Badania wyjaśniające z użyciem metod odpornych	Testowanie hipotez w badaniach
Dziedzina	Projektowanie badań	Projektowanie badań
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward	Early 20th century (Fisher 1925; Neyman–Pearson 1933)
Twórca	Peter J. Huber (robust statistics); applied to explanatory designs via Rand Wilcox and others	Karl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson
Typ	Quantitative research design	Quantitative confirmatory research design
Źródło pierwotne	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417603
Inne nazwy	robust causal research, outlier-resistant explanatory design, robust regression-based explanatory study	hypothetico-deductive research, confirmatory quantitative research, null hypothesis significance testing, NHST design
Pokrewne	4	4
Podsumowanie	Robust explanatory research combines the explanatory goal of identifying why and how variables causally influence one another with robust statistical methods that remain valid when data violate classical assumptions — particularly normality, homoscedasticity, and the absence of influential outliers. Rather than discarding outliers or forcing data to conform to ordinary least squares assumptions, this design applies estimators and inferential procedures that down-weight or resist the distorting influence of extreme observations while preserving the explanatory aim of the study.	Hypothesis testing research is a quantitative design in which the investigator derives one or more explicit, falsifiable propositions from theory, translates them into a null hypothesis (H0) and an alternative hypothesis (H1), collects empirical data, and then applies an inferential statistical test to decide whether the evidence is sufficient to reject H0. The approach is the dominant paradigm for confirmatory science across the social, behavioral, health, and natural sciences.