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If the p-value of a statistical test is .038 and the level of significant chosen for the hypothesis tst if .05 then.

I believe the correct answer is "do not reject" since .038 is less than .05 -

can anyone tell me if this answer is correct?

I believe the correct answer is "do not reject" since .038 is less than .05 -

can anyone tell me if this answer is correct?

When doing any statistical test, you can view the p-value as the "chance" that the null hypothesis is true. Now as a convention in most sciences we say that if the "chance" or p-value drops below 0.05 (or 5%) we can safely assume that it is not true.

In other situations and in esp. medical science the "critical" P-value is lower (say 0.01 percent, because peoples lives could be at stake). So we are (more) sure we dont reject the null hypothesis incorrectly.

So basically just remember that the p-value represents the "likelihood" or "chance" that the null hypothesis true.

That's actually a common misconception...

The p-value represents the probability of obtaining the value or larger value of the statistic, if in fact the null hypothesis is true.

In other words, it is representative of the weight of evidence in support of rejecting the null --> if the p-value is small, the evidence against the null is strong, if the p-value is large, the evidence against the null is weak.

The null hypothesis is either true or not (it is merely a theory phrased in a way that allows it to be tested), and does not have a probability associated with it....Therefore the p-value is definitely not the chance that the null is true.

That's actually a common misconception...

The p-value represents the probability of obtaining the value or larger value of the statistic, if in fact the null hypothesis is true.

That’s why I used the quotation marks when writing “chance” above.

It's even worse: the p-value represents the probability of obtaining a particular value or a greater value of the statistic based on a parametric model you parameterized with your sample values. Therefore it represents the fraction of a theoretical population that is equal to or greater than the value of interest.

However I have found that when explaining all that to people who really hate stats, it becomes even more confusing. In the beginning all the terms and definitions seem overwhelming so I simply use this little "trick" and it really seems to help people remember it (ergo pass the test). Although it's not completely true, I always think that once they get more adept in stats they will figure it out. I view it as the lesser or two evils.

However you are probably far more experienced in teaching stats so if you have a good argument why I should not use this trick for people just beginning to learn stats I’m all ear.

thanks,

Edit:

However after talking it over with a peer I found that I really should refrain from using the word chance.

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:yup:

That’s why I used the quotation marks when writing “chance” above.

It's even worse: the p-value represents the probability of obtaining a particular value or a greater value of the statistic based on a parametric model you parameterized with your sample values. Therefore it represents the fraction of a theoretical population that is equal to or greater than the value of interest.

...

That’s why I used the quotation marks when writing “chance” above.

It's even worse: the p-value represents the probability of obtaining a particular value or a greater value of the statistic based on a parametric model you parameterized with your sample values. Therefore it represents the fraction of a theoretical population that is equal to or greater than the value of interest.

...

Why can't you just say it like this (though I wouldn't use the word larger)...

The p-value represents the probability of obtaining the value or larger value of the statistic, if in fact the null hypothesis is true.

Obviously this is over complicating things.

Why can't you just say it like this (though I wouldn't use the word larger)...

Why can't you just say it like this (though I wouldn't use the word larger)...

I am of course not talking about the official definition of the P-value but just a trick to help a person remember which hypothesis should be rejected.

Like the other simplified explanations this is NOT a definition, but it helped me when I was first trying to understand the meaning of the prober definitions.

Saying the treatment works when it actually doesn't also means making a type 1 error.

Sometimes we treat people as if they have the "right" to understand probability and statistics, but that's like me claiming I have the "right" to understand theoretical physics, and clearly I don't..

...and I don't expect anyone to be able to explain it to me in terms I can understand....

we could go on forever, so I think I'll close this post...

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