Critical Value & P-Value Methods: Comparison & Relationships

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Mutually exclusive events cannot occur simultaneously, while independent events do not influence each other’s outcomes, making it impossible for two events to be both mutually exclusive and independent. The critical value method determines the threshold for rejecting the null hypothesis, while the p-value method quantifies the evidence against the null hypothesis. Both methods are essential for significance testing in statistics. The critical value represents the maximum test statistic that allows for the null hypothesis to remain accepted, whereas the p-value indicates the strength of evidence against it. Understanding these concepts is crucial for effective hypothesis testing.
jokerwild
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1)
In the basic probability theory the concepts of mutually exclusive events and independent events share prominent roles, can two events be both mutually exclusive and independent at the same time?

2)
What are two vital concepts and the relationship between "Critical Value Method" and the "P-Value Method"?
 
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Ooh, looks like homework...
 
jokerwild said:
1)
In the basic probability theory the concepts of mutually exclusive events and independent events share prominent roles, can two events be both mutually exclusive and independent at the same time?
What is the DEFINITION of "mutually exclusive"?
What is the DEFINITION of "independent"?

2)
What are two vital concepts and the relationship between "Critical Value Method" and the "P-Value Method"?
Have you considered looking those up in your textbook
 
For the second question I came up with this:

The critical value method is used for significance testing and is the value that a test must exceed in order for the null hypothesis to be rejected. The critical value would be the largest value of the test statistic that would end in the refusal of the null hypothesis. The p-value method is a measure of how much evidence you have against or for the null hypotheses.

Would this be a valid answer?
 
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