Descriptive statistics - wikipedia Descriptive statistics A descriptive statistic ( in the count noun sense ) is a summary statistic that quantitatively describes or summarizes features of a collection of information, while descriptive statistics in the mass noun sense is the process of using and analyzing those statistics. Descriptive statistics is distinguished from inferential statistics ( or inductive statistics ), in that descriptive statistics aims to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. This generally means that descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory, and are frequently nonparametric statistics. Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented. For example, in papers reporting on human subjects, typically a table is included giving the overall sample size, sample sizes in important subgroups ( e.g., for each treatment or exposure group ), and demographic or clinical characteristics such as the average age, the proportion of subjects of each sex, the proportion of subjects with related comorbidities, etc. Some measures that are commonly used to describe a data set are measures of central tendency and measures of variability or dispersion. Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation ( or variance ), the minimum and maximum values of the variables, kurtosis and skewness. Contents 1 Use in statistical analysis 1.1 Univariate analysis 1.2 Bivariate and multivariate analysis 2 References 3 External links Use in statistical analysis Descriptive statistics provide simple summaries about the sample and about the observations that have been made. Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple - to - understand graphs. These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation. For example, the shooting percentage in basketball is a descriptive statistic that summarizes the performance of a player or a team. This number is the number of shots made divided by the number of shots taken. For example, a player who shoots 33 % is making approximately one shot in every three. The percentage summarizes or describes multiple discrete events. Consider also the grade point average. This single number describes the general performance of a student across the range of their course experiences. The use of descriptive and summary statistics has an extensive history and, indeed, the simple tabulation of populations and of economic data was the first way the topic of statistics appeared. More recently, a collection of summarisation techniques has been formulated under the heading of exploratory data analysis : an example of such a technique is the box plot. In the business world, descriptive statistics provides a useful summary of many types of data. For example, investors and brokers may use a historical account of return behavior by performing empirical and analytical analyses on their investments in order to make better investing decisions in the future. Univariate analysis Univariate analysis involves describing the distribution of a single variable, including its central tendency ( including the mean, median, and mode ) and dispersion ( including the range and quartiles of the data - set, and measures of spread such as the variance and standard deviation ). The shape of the distribution may also be described via indices such as skewness and kurtosis. Characteristics of a variable 's distribution may also be depicted in graphical or tabular format, including histograms and stem - and - leaf display. Bivariate and multivariate analysis When a sample consists of more than one variable, descriptive statistics may be used to describe the relationship between pairs of variables. In this case, descriptive statistics include : Cross-tabulations and contingency tables Graphical representation via scatterplots Quantitative measures of dependence Descriptions of conditional distributions The main reason for differentiating univariate and bivariate analysis is that bivariate analysis is not only simple descriptive analysis, but also it describes the relationship between two different variables. Quantitative measures of dependence include correlation ( such as Pearson 's r when both variables are continuous, or Spearman 's rho if one or both are not ) and covariance ( which reflects the scale variables are measured on ). The slope, in regression analysis, also reflects the relationship between variables. The unstandardised slope indicates the unit change in the criterion variable for a one unit change in the predictor. The standardised slope indicates this change in standardised ( z - score ) units. Highly skewed data are often transformed by taking logarithms. Use of logarithms makes graphs more symmetrical and look more similar to the normal distribution, making them easier to interpret intuitively. References Jump up ^ Mann, Prem S. ( 1995 ). Introductory Statistics ( 2nd ed. ). Wiley. ISBN 0 - 471 - 31009 - 3. Jump up ^ Dodge, Y. ( 2003 ). The Oxford Dictionary of Statistical Terms. OUP. ISBN 0 - 19 - 850994 - 4. Jump up ^ Investopedia, Descriptive Statistics Terms Jump up ^ Trochim, William M.K. ( 2006 ). `` Descriptive statistics ''. Research Methods Knowledge Base. Retrieved 14 March 2011. Jump up ^ Babbie, Earl R. ( 2009 ). The Practice of Social Research ( 12th ed. ). Wadsworth. pp. 436 -- 440. ISBN 0 - 495 - 59841 - 0. Jump up ^ Nick, Todd G. ( 2007 ). `` Descriptive Statistics ''. Topics in Biostatistics. Methods in Molecular Biology. 404. New York : Springer. pp. 33 -- 52. doi : 10.1007 / 978 - 1 - 59745 - 530 - 5_3. ISBN 978 - 1 - 58829 - 531 - 6. External links Descriptive Statistics Lecture : University of Pittsburgh Supercourse : http://www.pitt.edu/~super1/lecture/lec0421/index.htm hide Statistics Outline Index hide Descriptive statistics Continuous data Center Mean arithmetic geometric harmonic Median Mode Dispersion Variance Standard deviation Coefficient of variation Percentile Range Interquartile range Shape Central limit theorem Moments Skewness Kurtosis L - moments Count data Index of dispersion Summary tables Grouped data Frequency distribution Contingency table Dependence Pearson product - moment correlation Rank correlation Spearman 's rho Kendall 's tau Partial correlation Scatter plot Graphics Bar chart Biplot Box plot Control chart Correlogram Fan chart Forest plot Histogram Pie chart Q -- Q plot Run chart Scatter plot Stem - and - leaf display Radar chart Data collection Study design Population Statistic Effect size Statistical power Sample size determination Missing data Survey methodology Sampling stratified cluster Standard error Opinion poll Questionnaire Controlled experiments Design control optimal Controlled trial Randomized Random assignment Replication Blocking Interaction Factorial experiment Uncontrolled studies Observational study Natural experiment Quasi-experiment Statistical inference Statistical theory Population Statistic Probability distribution Sampling distribution Order statistic Empirical distribution Density estimation Statistical model L space Parameter location scale shape Parametric family Likelihood ( monotone ) Location -- scale family Exponential family Completeness Sufficiency Statistical functional Bootstrap U V Optimal decision loss function Efficiency Statistical distance divergence Asymptotics Robustness Frequentist inference Point estimation Estimating equations Maximum likelihood Method of moments M - estimator Minimum distance Unbiased estimators Mean - unbiased minimum - variance Rao -- Blackwellization Lehmann -- Scheffé theorem Median unbiased Plug - in Interval estimation Confidence interval Pivot Likelihood interval Prediction interval Tolerance interval Resampling Bootstrap Jackknife Testing hypotheses 1 - & 2 - tails Power Uniformly most powerful test Permutation test Randomization test Multiple comparisons Parametric tests Likelihood - ratio Wald Score Specific tests Z - test ( normal ) Student 's t - test F - test Goodness of fit Chi - squared G - test Kolmogorov -- Smirnov Anderson -- Darling Lilliefors Jarque -- Bera Normality ( Shapiro -- Wilk ) Likelihood - ratio test Model selection Cross validation AIC BIC Rank statistics Sign Sample median Signed rank ( Wilcoxon ) Hodges -- Lehmann estimator Rank sum ( Mann -- Whitney ) Nonparametric anova 1 - way ( Kruskal -- Wallis ) 2 - way ( Friedman ) Ordered alternative ( Jonckheere -- Terpstra ) Bayesian inference Bayesian probability prior posterior Credible interval Bayes factor Bayesian estimator Maximum posterior estimator Correlation Regression analysis Correlation Pearson product - moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression model validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines ( MARS ) Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard predictors Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity Generalized linear model Exponential families Logistic ( Bernoulli ) / Binomial / Poisson regressions Partition of variance Analysis of variance ( ANOVA, anova ) Analysis of covariance Multivariate ANOVA Degrees of freedom Categorical / Multivariate / Time - series / Survival analysis Categorical Cohen 's kappa Contingency table Graphical model Log - linear model McNemar 's test Multivariate Regression Manova Principal components Canonical correlation Discriminant analysis Cluster analysis Classification Structural equation model Factor analysis Multivariate distributions Elliptical distributions Normal Time - series General Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality Specific tests Dickey -- Fuller Johansen Q - statistic ( Ljung -- Box ) Durbin -- Watson Breusch -- Godfrey Time domain Autocorrelation ( ACF ) partial ( PACF ) Cross-correlation ( XCF ) ARMA model ARIMA model ( Box -- Jenkins ) Autoregressive conditional heteroskedasticity ( ARCH ) Vector autoregression ( VAR ) Frequency domain Spectral density estimation Fourier analysis Wavelet Whittle likelihood Survival Survival function Kaplan -- Meier estimator ( product limit ) Proportional hazards models Accelerated failure time ( AFT ) model First hitting time Hazard function Nelson -- Aalen estimator Test Log - rank test Applications Biostatistics Bioinformatics Clinical trials / studies Epidemiology Medical statistics Engineering statistics Chemometrics Methods engineering Probabilistic design Process / quality control Reliability System identification Social statistics Actuarial science Census Crime statistics Demography Econometrics National accounts Official statistics Population statistics Psychometrics Spatial statistics Cartography Environmental statistics Geographic information system Geostatistics Kriging Portal Commons WikiProject Statistics portal GND : 4070313 - 7 Retrieved from `` https://en.wikipedia.org/w/index.php?title=Descriptive_statistics&oldid=841076069 '' Categories : Summary statistics Psychometrics Descriptive statistics Hidden categories : Wikipedia articles with GND identifiers Talk Contents Interaction About Wikipedia Беларуская Català Deutsch Ελληνικά Español Euskara فارسی Français Galego 한국어 Bahasa Indonesia Italiano עברית Basa Jawa Latviešu Lëtzebuergesch Magyar Македонски Norsk Polski Português Română Русский Simple English Српски / srpski Basa Sunda Tagalog ไทย Türkçe Українська Tiếng Việt ייִדיש 中文 25 more Edit links This page was last edited on 13 May 2018, at 22 : 48 ( UTC ). 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Question: descriptive statistics are best described as methods that help researchers
Answer: to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent