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.

**Related Pages**

16: Sample Proportions & Confidence Intervals About The Mean

17: t-Distribution & Confidence Intervals 2

19: Effect Size, Power, Statistical vs. Practical Significance

20: One Sample z-Test & One Sample t-Test

Jump to Table of Contents

Null Hypothesis (H_{0})

- The assumption you’re beginning with.
- The opposite of what you’re testing.
Alternative Hypothesis (H
_{1}) - The claim you’re testing.

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)

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%)

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