পদ্ধতির তুলনা করুন

	অনুপাত পরীক্ষার জন্য শক্তি বিশ্লেষণ	সঠিক দ্বিপদী পরীক্ষা (Exact Binomial Test)	স্বাধীনতার কাই-স্কয়ার পরীক্ষা	ANOVA-র জন্য পাওয়ার বিশ্লেষণ
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Hypothesis test	Regression model	Hypothesis test	Hypothesis test
উদ্ভবের বছর	1988	1988	1900	1988
প্রবর্তক	Jacob Cohen	Classical exact test; textbook treatment by Siegel & Castellan	Karl Pearson	Jacob Cohen
ধরন	Sample size determination	Exact one-sample test for a proportion	Nonparametric test of association	Sample size determination
মৌলিক উৎস	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.	Siegel, S. & Castellan, N. J. (1988). Nonparametric Statistics for the Behavioral Sciences (2nd ed.). McGraw-Hill. ISBN: 978-0070573574	Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
অপর নাম	proportion power analysis, two-proportion z-test power, z-test for proportions power, Oran Testi Güç Analizi	exact binomial test, binomial probability test, exact test for a proportion, Tam Binom Testi	chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi	ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA
সম্পর্কিত	3	2	2	4
সারসংক্ষেপ	Power analysis for proportion tests is a prospective sample-size planning method used to determine how many participants are needed to detect a meaningful difference between two (or one) proportions with a specified probability. Formalised by Jacob Cohen in his 1988 landmark text, it applies the arcsine transformation to convert proportions into the effect-size index h, enabling direct calculation of the required sample size.	The exact binomial test checks whether the observed number of successes in a fixed number of independent trials is consistent with a pre-specified success probability p₀. Because it computes exact binomial tail probabilities rather than relying on a normal approximation, it is the gold standard for testing a proportion in small samples; this two-sided formulation follows Siegel & Castellan's classic treatment (1988).	The chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900.	Power analysis for ANOVA is a prospective statistical technique that determines the minimum sample size needed to detect a specified group mean difference with a chosen probability. Formalized by Jacob Cohen in his 1988 monograph, it translates a researcher's effect size expectation — expressed as Cohen's f — along with the desired Type I error rate (alpha) and statistical power (1 − beta) into a concrete per-group sample size recommendation for one-way or factorial ANOVA designs.