Understanding P-values in Statistical Analysis

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In summary, The p-value of 0.882 is larger than the typical significance level of 0.05, indicating that we fail to reject the null hypothesis. This means that the data is consistent with the prediction based on the null hypothesis. The p-value represents the probability of obtaining a test statistic at least as extreme as the one observed.
  • #1
whitehorsey
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3. I solved for t distribution = (.03 - 0)/(.1/√25) = .15
P - Value = 2P(T ≥ .15) = 2(.441) = .882. So I got No but instead we fail to reject H0 because 0.882 > .05 (.005 is the typical significance level). Is this correct and would it be a correct interpretation?
 
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  • #2
I am surprised that s does not have a unit.

0.882 > 0.05 (or whatever you want to use), so we don't reject H0.
The large p-value shows that the result is very close to the "prediction" based on H0.
 
  • #3
mfb said:
I am surprised that s does not have a unit.

0.882 > 0.05 (or whatever you want to use), so we don't reject H0.
The large p-value shows that the result is very close to the "prediction" based on H0.

Thank You!
 
  • #4
And do you know in words what that p-value means?
 
  • #5
abeliando said:
And do you know in words what that p-value means?

The probability of attaining a test statistic.
 

What is a p-value and why is it important in scientific research?

A p-value, or probability value, is a statistical measure that indicates the likelihood of obtaining a result equal to or more extreme than the one observed, assuming that the null hypothesis is true. In scientific research, the p-value is used to determine the significance of an experimental result. A low p-value (usually less than 0.05) suggests that the observed result is unlikely to occur by chance and therefore supports the alternative hypothesis.

What does a p-value of 0.05 mean?

A p-value of 0.05 means that there is a 5% chance of obtaining the observed result by chance alone, assuming that the null hypothesis is true. In other words, if the null hypothesis is true, there is only a 5% chance that the observed result is not due to random variation. This is commonly used as the threshold for determining statistical significance in scientific research.

Can a p-value be greater than 1?

No, a p-value cannot be greater than 1. The p-value represents the probability of obtaining the observed result or a more extreme result if the null hypothesis is true. As probabilities cannot exceed 1, the p-value also cannot exceed 1.

What is the difference between statistical significance and practical significance?

Statistical significance refers to the likelihood of obtaining the observed result by chance alone, assuming that the null hypothesis is true. Practical significance, on the other hand, refers to the real-world or practical implications of the observed result. A result may be statistically significant but not necessarily practically significant, meaning that the effect observed may be too small to have any practical importance or impact.

Can a p-value be used to prove a hypothesis?

No, a p-value cannot be used to prove a hypothesis. The p-value can only provide evidence for or against the null hypothesis, but it cannot prove it. Other factors, such as study design, sample size, and effect size, must also be considered in determining the validity of a hypothesis.

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