Compară metode

	Statistici Descriptive	Test Kolmogorov-Smirnov
Domeniu	Statistică	Statistică
Familie	Hypothesis test	Hypothesis test
Anul apariției	1977	1933
Autorul original	John W. Tukey	Andrey Nikolaevich Kolmogorov; Nikolai Vasilyevich Smirnov
Tip	Summary procedure	Nonparametric goodness-of-fit test
Sursa seminală	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.
Denumiri alternative	summary statistics, exploratory data summary, Betimsel İstatistik	KS test, K-S test, one-sample KS test, Kolmogorov-Smirnov Testi
Înrudite	6	2
Rezumat	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.