# Basic Statistics Terminology

- Posted by Data Science Anywhere
- Categories Statistics
- Tags census, sampling techniques, Statistics, survey

## Statistics Big Picture

Statistics provides a way of organizing data to extract information on a wider and objective basis than relying on personal experience

- Data Gathering
- Data Understanding
- Data Analysis/Interpretation
- Data Presentation

## Population & Sample

**Census:** Gathering data from the whole **population **of interest.

For example,

elections, 10-year census, etc.

**Survey: **Gathering data from the **sample **in order to make conclusions about the population.

For example,

opinion polls, quality control checks in manufacturing units, etc.

## Data Gathering or Sampling Techniques

There are four types of sampling techniques

**Convenience Sampling**

Convenience sampling is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. This type of sampling is most useful for pilot testing.

Eg: Online Polls, Asking your friends etc.

**2. Random Sampling**

Each member has equal chance of being selected.

3.** Systematic Random Sampling**

Example: Supermarket chooses every 10th or 15th customer entering the supermarket and conduct the survey.

**4. Stratified Sampling**

Divide the data into several relevant **strata **and then sample from each strata

Eg: For getting an opinion on demonetization, one choice of strata might be state-wise analysis. We get 20 random volunteers from each and every state.

5. **Cluster Sampling**

Divide the population in to groups or clusters. Then select a one or a few clusters and survey **everyone **from the chosen subset.

## Parameter & Statistic

**Parameter: **A descriptive measure of the **population**. For example, population mean, population variance, population standard deviation, etc.

**Statistic: **A descriptive measure of the **sample**. For example, sample mean, sample variance, sample standard deviation, etc.

Mean – μ

Variance – σ^{2}

Standard Deviation – σ

Mean – x

Variance – s^{2}

Standard Deviation – s

## Descriptive & Inferential Statistics

**Descriptive Statistics: **Data gathered about a group to reach conclusion about the same group.

**Inferential Statistics: **Data gathered from a sample and the statistics generated to reach conclusion about the population from which the sample is taken. Also known as Inductive Statistics

These are the basic terminologies that are required for Statistics for Data Science