What if the p-value is zero?
A p-value simply tells you the strength of evidence in support of a null hypothesis. If the p-value is less than the significance level, we reject the null hypothesis. So, when you get a p-value of 0.000, you should compare it to the significance level. Common significance levels include 0.1, 0.05, and 0.01.
Is 0.000 statistically significant?
The Sig. value is reported to be 0.000. This indicates that it is less than 0.001 (but not exactly 0), which, in turn, means that it is less than our chosen significance level of 0.01. Thus, we can regard the null hypothesis as refuted and start believing that there really is an association.
Can a P value ever be negative?
By axioms of probability p-value should not be negative being a probability. It is clear that your algorithm may be erroneous. Use any software for your calculation and compare the results.
Can p-value be 0 in Anova?
A p-value of true zero is possible, but not in any context you’re likely to see. It means ‘this evidence is literally impossible under the null hypothesis’. Example: If your null is “unicorns don’t exist”, and you observe one, you have p-value = 0.
When do you reject the p value?
As computers became readily available, it became common practice to report the observed significance level (or P value)–the smallest fixed level at which the the null hypothesis can be rejected. If your personal fixed level is greater than or equal to the P value, you would reject the null hypothesis.
What does p value tell you?
A p-value can tell you that a difference is statistically significant, but it tells you nothing about the size or magnitude of the difference. “The p-value is low, so the alternative hypothesis is true.”.
What does p value tell us?
The p-value tells us about the likelihood or probability that the difference we see in sample means is due to chance. Thus, it really is an expression of probability, with a value ranging from zero to one.
How do you determine the p value?
Steps Determine your experiment’s expected results. Determine your experiment’s observed results. Determine your experiment’s degrees of freedom. Compare expected results to observed results with chi square. Choose a significance level. Use a chi square distribution table to approximate your p-value.