手法を比較

	正確二項検定	2標本の比率のz検定	符号検定 (Sign Test)
分野	統計学	統計学	統計学
系統	Regression model	Hypothesis test	Hypothesis test
提唱年	1988	1900	1946
提唱者	Classical exact test; textbook treatment by Siegel & Castellan	Karl Pearson / classical large-sample z approximation	W. J. Dixon & A. M. Mood
種類	Exact one-sample test for a proportion	Parametric proportion comparison	Nonparametric median test
原典	Siegel, S. & Castellan, N. J. (1988). Nonparametric Statistics for the Behavioral Sciences (2nd ed.). McGraw-Hill. ISBN: 978-0070573574	Fleiss, J. L., Levin, B., & Paik, M. C. (2003). Statistical Methods for Rates and Proportions (3rd ed.). Wiley.	Dixon, W. J. & Mood, A. M. (1946). The statistical sign test. Journal of the American Statistical Association, 41(236), 557–566.
別名	exact binomial test, binomial probability test, exact test for a proportion, Tam Binom Testi	z-test for proportions, two-sample proportion test, one-proportion z-test, Oran Testi — z Testi (Oranlar)	İşaret Testi (Sign Test), one-sample sign test, paired sign test
関連	2	4	4
概要	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 proportion test (z-test for proportions) is a parametric hypothesis test that compares one or two sample proportions against a reference value or each other. Grounded in the large-sample normal approximation formalized by Fleiss, Levin, and Paik (2003), it is the standard tool for binary outcome comparisons when samples are large enough for the central limit theorem to apply.	The sign test is the simplest nonparametric hypothesis test for deciding whether the median of paired differences — or of a single sample — differs significantly from a hypothesised value. Formalised by W. J. Dixon and A. M. Mood in 1946, it imposes virtually no distributional assumptions and can be applied to any data where individual differences can be classified as positive or negative.