Basic Statistics Terminology

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

1. 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 – σ²

Standard Deviation – σ

 

Mean – x

Variance – s²

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