# Statistics Lectures - 18: Hypothesis Testing

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

This is the eighteenth page of the series of free video lessons, “Statistics Lectures”. These lectures discuss hypothesis testing, looking at the concepts of null and alternative hypotheses, type I and type II errors and one-tailed versus two-tailed tests.

### Statistics - Lecture 52: Null And Alternative Hypotheses

Null Hypothesis (H0)

• The assumption you’re beginning with.
• The opposite of what you’re testing. Alternative Hypothesis (H1)
• The claim you’re testing.

### Statistics - Lecture 53: Type I And Type II Errors

Outcome 1: We reject the Null Hypothesis when in reality, it is false (Good)
Outcome 2: We reject the Null Hypothesis when in reality, it is true (Type I Error)
Outcome 3: We retain the Null Hypothesis when in reality, it is false (Type II Error)

### Statistics - Lecture 54: One-Tailed And Two-Tailed Tests

Two-Tailed Test
Testing to see if your calculated value is either above or below wher it’s expected to be. One-Tailed Test
Testing only to see if your calculated value is above where it’s expected to be.
Or
Testing only to see if your calculated value is below where it’s expected to be.

Example:
School District A states that its high schools have an 85% passage rate on the High School Exit Exam. A new school was recently opened in the district, and it was found that a sample of 150 students had a passage rate of 88%, with a standard deviation of 4%.
Does this new school have a different passage rate than the rest of School District A? Does the school have a different passage rate different than 85%? (This is a two-tailed test, because we are testing to see if the mean is either below or above 85%).
Does this new school have a different passage rate greater than 85%? (This is a one-tailed test, because we are testing to see if the mean is greater than 85%) 