השוואת שיטות

	סטטיסטיקה תיאורית	מבחן קולמוגורוב-סמירנוב
תחום	סטטיסטיקה	סטטיסטיקה
משפחה	Hypothesis test	Hypothesis test
שנת המקור	1977	1933
הוגה השיטה	John W. Tukey	Andrey Nikolaevich Kolmogorov; Nikolai Vasilyevich Smirnov
סוג	Summary procedure	Nonparametric goodness-of-fit test
מקור מכונן	Tukey, J.W. (1977). Exploratory Data Analysis. Addison-Wesley. ISBN: 978-0201076165	Kolmogorov, A. N. (1933). Sulla determinazione empirica di una legge di distribuzione. Giornale dell'Istituto Italiano degli Attuari, 4, 83–91.
כינויים	summary statistics, exploratory data summary, Betimsel İstatistik	KS test, K-S test, one-sample KS test, Kolmogorov-Smirnov Testi
קשורות	6	2
תקציר	Descriptive statistics is a set of procedures that numerically and visually summarises the essential characteristics of a dataset: central tendency (mean, median, mode), spread (standard deviation, interquartile range), shape (skewness, kurtosis), and frequency distributions. Systematised for applied data analysis by John W. Tukey in his 1977 work on Exploratory Data Analysis, descriptive statistics serves as the indispensable first step before any inferential or modelling procedure.	The Kolmogorov-Smirnov (KS) test is a nonparametric goodness-of-fit test that assesses whether a sample comes from a specified theoretical distribution, such as the normal or exponential. First formalised by Andrey Kolmogorov in 1933 and further developed by Nikolai Smirnov in 1948, it compares the empirical cumulative distribution function of the observed data against a target theoretical CDF and quantifies their maximum absolute deviation.