Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.
This key competency area deals with samples, outliers, statistical bias, and common distributions, among others.
- Samples - Ability to obtain a sample mean. Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger group. A sample is refers to a smaller, manageable version of a larger group.
- Outliers - An understanding of an outlier, an observation that lies an abnormal distance from other values in a random sample.
- Statistical bias - Statistical bias is a feature of a statistical technique or of its results whereby the expected value of the results differs from the true underlying quantitative parameter being estimated. Basic familiarity with Selection bias, Survivorship bias, Omitted variable bias, Recall bias, Observer bias, and Funding bias.
- Common distributions - The distribution of a statistical data set is a listing or function showing all the possible values (or intervals) of the data and how often they occur. An understanding of basic types of distribution such as Bernoulli Distribution, Uniform Distribution, Binomial Distribution, Normal Distribution, Poisson Distribution, and Exponential Distribution.
- Central limit theorem - Familiarity with applying the Central Limit Theorem (CLT), which is a statistical concept that states that the sample mean distribution of a random variable will assume a near-normal or normal distribution if the sample size is large enough.