Hi All

*N.b. title should read t-test*

1st post, I need some help interpreting the results of a t-test. I understand that t-tests normally only reject the null hypothesis if p<0.05. This is commonly described as being "statistically significant". However, I am interested in what statements can be made if the p value is higher than 0.05.

If I do a t-test and find that the p value is 0.02 can I say that the probability of the data I have tested (e.g. comparing two mean values derived from two groups of scores on exam results) being due to chance is 2%?

Can I then say that the probability of it being due to factors other than chance is 98%?

And to continue this logic, can I then say that a p value of 0.30 indicates that there is a 30% chance that the difference observed is due to chance, and hence there is a 70% chance that it is due to factors other than chance?

Can I design a test with the parameter set at 0.50? If I get a P value of 0.4 can I then accept my null hypothesis and state that there is a 60% chance that the data is due to factors other than chance?

I know statisticians have a love affair with 95% confidence intervals, but surely the p value tells us something below this level, even if it offers a lot less certainty than at 95%?

Thanks, all views appreciated!

*N.b. title should read t-test*

1st post, I need some help interpreting the results of a t-test. I understand that t-tests normally only reject the null hypothesis if p<0.05. This is commonly described as being "statistically significant". However, I am interested in what statements can be made if the p value is higher than 0.05.

If I do a t-test and find that the p value is 0.02 can I say that the probability of the data I have tested (e.g. comparing two mean values derived from two groups of scores on exam results) being due to chance is 2%?

Can I then say that the probability of it being due to factors other than chance is 98%?

And to continue this logic, can I then say that a p value of 0.30 indicates that there is a 30% chance that the difference observed is due to chance, and hence there is a 70% chance that it is due to factors other than chance?

Can I design a test with the parameter set at 0.50? If I get a P value of 0.4 can I then accept my null hypothesis and state that there is a 60% chance that the data is due to factors other than chance?

I know statisticians have a love affair with 95% confidence intervals, but surely the p value tells us something below this level, even if it offers a lot less certainty than at 95%?

Thanks, all views appreciated!

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