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| Pearson의 독립성 카이제곱 검정× | 맥니마르 검정× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 1900 | 1947 |
| 창시자≠ | Karl Pearson | Quinn McNemar |
| 유형≠ | Nonparametric association / goodness-of-fit | Nonparametric test for paired binary data |
| 원전≠ | Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables. Philosophical Magazine, Series 5, 50(302), 157–175. link ↗ | McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157. DOI ↗ |
| 별칭 | chi-squared test, χ² test, Ki-Kare Testi, chi-square test | McNemar chi-square test, test for correlated proportions, paired binary test, McNemar Testi |
| 관련≠ | 3 | 5 |
| 요약≠ | The chi-square test of independence is a nonparametric hypothesis test that determines whether two categorical variables are statistically associated or independent of one another. Introduced by Karl Pearson in 1900, it remains the standard procedure for analysing contingency tables and requires no assumption of normality — only that observations are independent and that expected cell frequencies are sufficiently large. | McNemar's test is a nonparametric hypothesis test that compares two paired (correlated) binary proportions, such as a yes/no measurement taken on the same subjects before and after an intervention. It was introduced by Quinn McNemar in 1947 and works on the 2×2 table of matched outcomes. |
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