Question

1, I know that if the p-value is bigger than the significance level, we can reject the null hypothesis, right?

2, What is the difference between p-value and t value?

Answer 1

1. No, it is wrong.

If P-Value is bigger than the level of significance(Alpha) then we accept the null hypothesis.

And if P-Value is less than level of significance the we reject the null hypothesis.

2.

t value is nothing but the test Statistics and P-value is nothing but the minimum level of significance.

Higher the t value there is higher chances to reject the null hypothesis.

Higher the p value there is very less chance to reject the null hypothesis.

A t-value (or any other test statistic) is mostly an intermediate step. It's basically some statistic that was proven, under some assumptions, to have a well-known distribution. Since we know the distribution of the test statistic under the null hypothesis.

A P-Value is quantitative way to show the result of hypothesis testing. It mainly helps to find out the probability of happening something. A null hypothesis is assumed, the P-value gives the likeliness to null hypothesis becoming true.

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