A series of free Statistics Lectures with examples & solutions in videos.

These lectures introduce descriptive and inferential statistics and discuss sampling methods.

Descriptive statistics summarizes a sample, giving us some ideas about the mean and spread of the data, etc. Inferential statistics use a sample or samples to describe what the population is like; in other words, infering the characteristics of the population from the sample/s.

This is the first page of the series of free video lessons, "Statistics Lectures".

Statistics is the science of collecting, organizing, and analyzing data.

Data is the facts or pieces of information.

There are two main types of statistics - Descriptive Statistics and Inferential Statistics.

Descriptive Statistics consists of organizing and summarizing data.

Inferential Statistics consists of using data you have measured to form conclusions.

Population is the group you are interested in studying.

Sample is a subset of the population.

How do we select from the population what goes into our sample?

Ways of representing sample and population sizes:

N - used to represent the size of your population.

n - used to represent the size of your sample.

The goal of sampling is to create a sample that is representative of the population it is being drawn from.

The most basic sampling method is Simple Random Sampling.

When performing simple random sampling, every member of the population (N) has equal chance of being selected for your sample (n). This is the best sampling method, as your sample is almost guaranteed to be representative of your population. However, it is hardly ever used due to being too impractical.

Another method of sampling is Stratified Sampling.

When performing stratified sampling, the population (N) is split into non-overlapping groups ("stata"), then random sampling is done on each group to form a sample (n).

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