How to Determine Whether to Accept or Reject a Hypothesis?

In summary, the conversation is about determining whether to accept or reject a null hypothesis with a given sample size, number of successes, and significance level. The confusion arises from the steps involved in this process, specifically the use of 22.5 and 23.5 in the calculations. It is suggested to use P(x̅ ≥ 23) for the calculation and to consider using a t distribution due to the fact that the values are integer valued.
  • #1
physicslady123
10
0
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Summary:: Confused on correction for contunuity

I'm confused about one step for questions like this where you have to determine to accept or reject the null hypothesis.
Sample size: 50
Number of successes: 23
Significance level: 10%
Null hypothesis: p = 0.4
Accepted hypothesis: p > 0.4

Solution:
μ = np
= (50)(0.4)
= 20

σ = √npq
= √(20)(0.6)
= 3.46

This is the part I'm confused on: To determine if I should accept/reject the hypothesis, would I do 1) or 2) or 3)

1) P ( x̅ >23)
= P ( x̅ > 23.5)
= 1-P(x̅<23.5)
and then solve

2) P ( x̅ >23)
= 1 - ( x̅ < 22.5)
and then solve

3) P ( x̅ < 23)
=P ( x̅ < 22.5)
and then solve
 
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  • #2
Why do you have to introduce 22.5 and 23.5 at all? Can't you just look at ##P(\bar{x} \geq 23)##?

What is your plan with the ##\sigma## you computed? Typically that means you are going to pretend that the values you are drawing are from some t distribution or something, and the fact that it's integer valued no longer is as much of an issue, since in the approximation the integers are not special values.
 
  • #3
Office_Shredder said:
Why do you have to introduce 22.5 and 23.5 at all? Can't you just look at ##P(\bar{x} \geq 23)##?

What is your plan with the ##\sigma## you computed? Typically that means you are going to pretend that the values you are drawing are from some t distribution or something, and the fact that it's integer valued no longer is as much of an issue, since in the approximation the integers are not special values.
I would guess that in the question the samples are drawn from a binomial distribution and so the fact that it is integer valued is important. This question belongs in the homework section, I'll get it moved.
 

1. What does it mean to reject a hypothesis?

Rejecting a hypothesis means that the data and evidence collected do not support the proposed explanation or prediction. It suggests that the results are not statistically significant and the hypothesis is not a valid explanation for the observed phenomenon.

2. How do you determine whether to accept or reject a hypothesis?

The decision to accept or reject a hypothesis is based on the statistical analysis of the data. This involves comparing the observed results to the expected results, and determining the probability of obtaining the observed results by chance. If the probability is low (usually less than 5%), the hypothesis is rejected. If the probability is high, the hypothesis is accepted.

3. Can a hypothesis be partially accepted or rejected?

No, a hypothesis cannot be partially accepted or rejected. It is either supported by the data and accepted, or not supported by the data and rejected. However, further research and experimentation may lead to modifications or revisions of the hypothesis.

4. What is the significance level in hypothesis testing?

The significance level, also known as alpha (α), is the probability of rejecting a true null hypothesis. It is typically set at 0.05 or 5%, meaning that there is a 5% chance of rejecting a true null hypothesis. This level is used to determine the threshold for accepting or rejecting a hypothesis.

5. Can a rejected hypothesis ever be proven to be true?

No, a rejected hypothesis cannot be proven to be true. It can only be rejected or not rejected based on the available data and evidence. However, new evidence or further research may lead to a different conclusion and the hypothesis may be revisited and potentially accepted in the future.

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