Which statements are correct?
P = 0.05 means there is only a 5% chance that the null hypothesis is true.
P = 0.05 means there is a 5% chance of a false positive conclusion.
P = 0.05 means there is a 95% chance that the results would replicate if the study were repeated.
P > 0.05 means there is no difference between groups.
P < 0.05 means you have proven your experimental hypothesis.
P < 0.05 means there is a 5% probability of getting your results (or more extreme results) if the null hypothesis is true.
We are assessing the difference between the mean muscle strength of two groups. The null hypothesis says the two means are equal; the alternative hypothesis says they are not equal. If we get a p-value of 0.02 and we have set our alpha level at 0.05, which of the following statements are correct?
The null hypothesis that the population means are equal is rejected.
The p-value is small, so the alternative hypothesis is true.
There is a 2% probability that no difference exists.
There is a 2% probability that the null hypothesis is true.
There is a 98% probability that a difference exists.
Since we have a low p-value, the observed difference is important.
Since we have a low p-value, the observed difference is large.
Why are the following statements wrong?
p = 0.05, null hypothesis only 5% chance true.
Nonsignificant difference (e.g., p > 0.05) = no difference between groups.
A statistically significant finding is clinically important.
Studies with p-values on opposite sides of 0.05 are conflicting.
Studies with the same p-value provide the same evidence against the null hypothesis.
P = 0.05 means that we have observed data that would occur only 5% of the time if the null hypothesis is true.
P-values are properly written as inequalities (e.g., "p < 0.05" when P = 0.015).
P = 0.05 means that if you reject the null hypothesis, the probability of a type I error is only 5%.
A scientific conclusion/treatment policy should be based on whether or not the p-value is significant.