The normal distribution, a very common probability density, useful because of the central limit theorem.
The normal distribution, a very common probability density, useful because of the central limit theorem.
Scatter plots are used in descriptive statistics to show the observed relationships between different variables, here using the Iris flower data set.
Gerolamo Cardano, a pioneer on the mathematics of probability.
Karl Pearson, a founder of mathematical statistics.
A least squares fit: in red the points to be fitted, in blue the fitted line.
Confidence intervals: the red line is true value for the mean in this example, the blue lines are random confidence intervals for 100 realizations.
In this graph the black line is probability distribution for the test statistic, the critical region is the set of values to the right of the observed data point (observed value of the test statistic) and the p-value is represented by the green area.
The confounding variable problem: X and Y may be correlated, not because there is causal relationship between them, but because both depend on a third variable Z. Z is called a confounding factor.
gretl, an example of an open source statistical package

In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution.

- Central tendency

Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other.

- Statistics
The normal distribution, a very common probability density, useful because of the central limit theorem.

1 related topic

Alpha

Example of samples from two populations with the same mean but different dispersion. The blue population is much more dispersed than the red population.

Statistical dispersion

Example of samples from two populations with the same mean but different dispersion. The blue population is much more dispersed than the red population.

In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed.

Dispersion is contrasted with location or central tendency, and together they are the most used properties of distributions.