Contents
What does it mean when the p-value is 0?
P value 0.000 means the null hypothesis is true. Anyway, if your software displays a p values of 0, it means the null hypothesis is rejected and your test is statistically significant (for example the differences between your groups are significant).
What does t test and p-value tell you?
The larger the absolute value of the t-value, the smaller the p-value, and the greater the evidence against the null hypothesis.
Is a high T-value good?
Higher values of the t-value, also called t-score, indicate that a large difference exists between the two sample sets. The smaller the t-value, the more similarity exists between the two sample sets. A large t-score indicates that the groups are different. A small t-score indicates that the groups are similar.
What is a high T value?
Higher values of the t-value, also called t-score, indicate that a large difference exists between the two sample sets. The smaller the t-value, the more similarity exists between the two sample sets. A large t-score indicates that the groups are different.
What is the true p value of a t-test?
The true p-value is 0.15264, which is pretty close to our estimated p-value of 0.15. We saw in this post that it’s possible to estimate the p-value of a t-test by hand using the t-Distribution table.
When to reject null hypothesis with p value?
If the p-value is less than some alpha level (common choices are .01, .05, and .10) then we can reject the null hypothesis and conclude that the mean height of the plants is not equal to 15 inches. However, it’s also possible to estimate the p-value of the test by hand using a t-Distribution table.
What does it mean when p value is too high?
High p-values indicate that your evidence is not strong enough to suggest an effect exists in the population. An effect might exist but it’s possible that the effect size is too small, the sample size is too small, or there is too much variability for the hypothesis test to detect it.
How does the t test establish the problem?
Mathematically, it establishes the problem by assuming that the means of the two distributions are equal (H₀: µ₁=µ₂). If the t-test rejects the null hypothesis (H₀: µ₁=µ₂), it indicates that the groups are highly probably different.