**Introduction :-**

Making an inference about a population from a sample.

The need for an inference: One-sample and two-sample examples

**One-sample situations:-**

- Average Phosphate levels in Blood should =< 4.8 mg/dl

- Health department only allows 5% of the toothpastes of each brand to be out of specification (ration of fluoride, abrasives, etc.)

- New garage is inflating repair costs for accidents. Insurance fraud is suspected.

**Two-sample situations**

- Changing the temperature in a foundry process to see if the mean number of defects decreases.

- Two different manufacturing process to compare variance of finished product in each batch.

- Are 10th standard girls taller than 10th standard boys in India.

**Sampling Distribution:-**

The overarching principle:

- Have a null and alternate hypothesis

- Do some basic calculations/arithmetic on the data to create a single number called the "test statistic"

- If we assume the null hypothesis to be true (and make some assumptions about the distributions of various variables), then the 'test statistic' should be no different than a single random draw from a specific probability distribution.

- Test the probability that the "test statistic" you calculated belongs to this theoretical distribution. This is the p-value !

- Ergo : Its D | H not H | D

Inferential Statistics - Single sample tests

Inferential Statistics-Two sample tests

## No comments:

## Post a Comment