What is the Significance of Testing Population Variance in a Sample?

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Homework Help Overview

The discussion revolves around the significance of testing population variance in a sample, specifically in the context of a voting scenario where the probability of votes for a candidate is being analyzed. Participants are exploring the statistical methods involved in hypothesis testing and the interpretation of probabilities associated with sample data.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants are questioning the rationale behind calculating cumulative probabilities versus exact probabilities in the context of hypothesis testing. There is also a discussion on the interpretation of p-values and the significance of observed data in relation to the null hypothesis.

Discussion Status

The conversation is ongoing, with participants providing insights into the nature of hypothesis testing and the importance of cumulative probabilities. Some guidance has been offered regarding the interpretation of statistical claims, but no consensus has been reached on the specific phrasing or methodology used in the original problem statement.

Contextual Notes

There appears to be some confusion regarding the definitions and assumptions underlying the hypothesis testing framework, particularly in relation to the sample size and the expected outcomes based on the null hypothesis.

gxc9800
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Homework Statement


Given that 8 person vote for Mr Tan , i can't understand why the probability is sum of 0 people vote for him until 8 people vote for him. Why not the probability = 22c8 ( (0.6)^8 )( (0.4)^14) ?

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gxc9800 said:
Why not the probability = 22c8 ( (0.6)^8 )( (0.4)^14) ?
The probability ##22c8 ( (0.6)^8 )( (0.4)^14)## is the likelihood of drawing exactly this sample assuming the claim is true. When testing the hypothesis, you are comparing the cumulative probability of this sample (at least 8 people), against the claim.
Note that the expected value, assuming his claim is true would be ##22(.6)=13.2## people. If you just take the probability of 13 people saying they support him, that will not be 50%, but the cumulative probability will be close to that.
 
hi, When testing the hypothesis, you are comparing the cumulative probability of this sample (at least 8 people), against the claim

why the author doesn't say 'the investigator selects a random smaple of 22 people and at most 8 people agree to vote for him ' instead?
 
The question is: How likely is it to find something at least as extreme as what was observed? That is normally what people mean when they talk about p-values and significance. It is not: What is the probability of pulling this sample assuming that H0 is true?
The author may not state it, but that is what is always meant by testing significance.
 

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