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Get Started Now- Intro Lesson11:09
- Lesson: 110:34
- Lesson: 212:21
- Lesson: 311:18

Instead of using the Standard Normal Distribution to find the Z-score relating to the significance level, all you need to do is put the Test-Statistic on the Standard Normal Distribution and see if the “smaller” area (called the P-Value) is less than the given significance level ($\alpha$).

$\cdot$ P-Value $\leq$ $\alpha$ $\Rightarrow$ $H_0$ is false

$\cdot$ P-Value > $\alpha$ $\Rightarrow$ Fail to reject $H_0$

$\cdot$ P-Value $\leq$ $\alpha$ $\Rightarrow$ $H_0$ is false

$\cdot$ P-Value > $\alpha$ $\Rightarrow$ Fail to reject $H_0$

- IntroductionIntroduction to P-value
- 1.
**Proportion Hypothesis Testing**

From a sample of 100 university students, it was found that 58 of them have taken a mathematics course throughout their university career. Test the claim that “Most university students have taken a math course”. Do this using the P-Value method with a significance level of $\alpha=0.05$. - 2.Cole and Thomas throw a house party and offer free beer to people who come dressed up in Christmas theme. Thomas makes the claim “75% of people who come to our party will dress up”. By the end of the night 102 people dressed up, and 48 people didn’t dress up. Using the P-Value method with a 95% confidence level is Thomas’ claim true?
- 3.120 cars were sampled and it was found that the average size of fuel tanks were 46L. The standard deviation of the size of fuel tanks for all cars is 5L. Test the claim that “the average size of a gas tank for a car is 45L” using the P-Value method with a significance level of $\alpha=0.01$

10.

Hypothesis Testing

10.1

Null hypothesis and alternative hypothesis

10.2

Proving claims

10.3

Confidence levels, significance levels and critical values

10.4

Test statistics

10.5

Traditional hypothesis testing

10.6

P-value hypothesis testing

10.7

Mean hypothesis testing with t-distribution

10.8

Type 1 and type 2 errors

10.9

Chi-Squared hypothesis testing

10.10

Analysis of variance (ANOVA)

10.11

Chi-square goodness of fit test

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