# Descriptive statistics

**descriptivedescriptive statisticstatisticsDecileone-fifthdescribingDescriptive analysesdescriptive statistics modelingdescriptivelylocally significant**

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.wikipedia

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### Statistical inference

**inferential statisticsinferenceinferences**

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.

Inferential statistics can be contrasted with descriptive statistics.

### Nonparametric statistics

**non-parametricnon-parametric statisticsnonparametric**

This generally means that descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory, and are frequently nonparametric statistics.

Nonparametric statistics includes both descriptive statistics and statistical inference.

### Variance

**sample variancepopulation variancevariability**

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. 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).

Variance has a central role in statistics, where some ideas that use it include descriptive statistics, statistical inference, hypothesis testing, goodness of fit, and Monte Carlo sampling.

### Median

**averagesample medianmedian-unbiased estimator**

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. 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 median is a popular summary statistic used in descriptive statistics, since it is simple to understand and easy to calculate, while also giving a measure that is more robust in the presence of outlier values than is the mean.

### Average

**Rushing averageReceiving averagemean**

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.

The mode, the median, and the mid-range are often used in addition to the mean as estimates of central tendency in descriptive statistics.

### Mean

**mean valueaveragepopulation mean**

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. 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).

In descriptive statistics, the mean may be confused with the median, mode or mid-range, as any of these may be called an "average" (more formally, a measure of central tendency).

### Summary statistics

**summary statisticSummarizationdata summarization**

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. Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple-to-understand graphs.

### Quantitative research

**quantitativequantitative methodsquantitative data**

Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple-to-understand graphs.

Quantitative data is any data that is in numerical form such as statistics, percentages, etc. The researcher analyses the data with the help of statistics and hopes the numbers will yield an unbiased result that can be generalized to some larger population.

### Statistics

**statisticalstatistical analysisstatistician**

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.

Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).

### Box plot

**boxplotbox and whisker plotadjusted boxplots**

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 descriptive statistics, a box plot or boxplot is a method for graphically depicting groups of numerical data through their quartiles.

### Univariate analysis

**Univariatesingle variable**

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).

Like other forms of statistics, it can be inferential or descriptive.

### Range (statistics)

**rangerangingsample range**

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).

However, in descriptive statistics, this concept of range has a more complex meaning.

### Standard deviation

**standard deviationssample standard deviationSD**

The mean and the standard deviation of a set of data are descriptive statistics usually reported together.

### Mode (statistics)

**modemodalmodes**

### Quartile

**quartileslower quartilelower and upper quartiles**

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).

As is the basic idea of descriptive statistics, when encountering an outlier, we have to explain this value by further analysis of the cause or origin of the outlier.

### Exploratory data analysis

**explorative data analysisexploratorydata analysis**

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.

### Count noun

**countablecountcount nouns**

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.

### Information

**informativeinputinputs**

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.

### Mass noun

**mass nounsuncountable noununcountable**

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.

### Sample (statistics)

**samplesamplesstatistical sample**

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.

### Statistical population

**populationsubpopulationsubpopulations**

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.

### Probability theory

**theory of probabilityprobabilityprobability theorist**

This generally means that descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory, and are frequently nonparametric statistics.

### Sample size determination

**sample sizeSampling sizessample**

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.

### Demography

**demographicdemographicsdemographer**

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.

### Comorbidity

**comorbidcomorbiditiesco-morbid**