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Unit 6, Inference for Categorical Data Proportions - Coggle Diagram
Unit 6, Inference for Categorical Data Proportions
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Types of Error
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Type I error is made when the null hypothesis is actually true but the alternative hypothesis is chosen
- Reject Ho when it is true
Type II error is made when failing to reject the null hypothesis when it is false
- Fail to reject Ho when it is false
Significance Test
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P<α
- Reject the null, there is convincing evidence for Ha(alternative)
P>α
- Fail to reject the null, there is no convincing evidence for Ha(alternative)
P-Value Interpretation
- If the [null hypothesis is true], the probability of getting [sample size] is [p-value]
Constructing significance test for population proportions
- State:
-Null Hypothesis
-Alternative Hypothesis
-Define Parameter
-Significance Level
- Plan:
-Random
-Independence
-Normality:np̂≥10 and n(1-p̂)≥10
- Do:
-1-sample z-test
-Normalcdf()
- Conclude:
-Reject/Fail to reject the null(Ho), Convincing evidence/No Convincing evidence for alternative(Ha)
1-sample z-test formula 
Constructing significance test for difference in two population proportions
- State:
-Ho: p1-p2=0
-Ha: p1-p2 >, < 0
- Plan
-Random
-Independence
-Normality:n1pc≥10 and n1(1-pc)≥10, n2pc≥10 and n2(1-pc)≥10
- Do
-2 Prop Z-Test
- Conclude
-Reject/Fail to reject the null(Ho), Convincing evidence/No Convincing evidence for alternative(Ha)
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