What does p-value 2.2e 16 means?

0.00000000000000022
2.2e-16 is the scientific notation of 0.00000000000000022, meaning it is very close to zero. Your statistical software probably uses this notation automatically for very small numbers.

Why reject null hypothesis when p-value is small?

The p-value is used as an alternative to rejection points to provide the smallest level of significance at which the null hypothesis would be rejected. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.

How do you know if you should reject the null hypothesis?

Failing to Reject the Null Hypothesis

1. When your p-value is less than or equal to your significance level, you reject the null hypothesis. The data favors the alternative hypothesis.
2. When your p-value is greater than your significance level, you fail to reject the null hypothesis. Your results are not significant.

What does it mean if p-value has e?

A closely related concept is the E-value, which is the expected number of times in multiple testing that one expects to obtain a test statistic at least as extreme as the one that was actually observed if one assumes that the null hypothesis is true. The E-value is the product of the number of tests and the p-value.

Why is my p-value so 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.

Is p-value of 0.1 significant?

The smaller the p-value, the stronger the evidence for rejecting the H0. This leads to the guidelines of p < 0.001 indicating very strong evidence against H0, p < 0.01 strong evidence, p < 0.05 moderate evidence, p < 0.1 weak evidence or a trend, and p ≥ 0.1 indicating insufficient evidence[1].

What does p-value of 0.5 mean?

Mathematical probabilities like p-values range from 0 (no chance) to 1 (absolute certainty). So 0.5 means a 50 per cent chance and 0.05 means a 5 per cent chance. In most sciences, results yielding a p-value of . 05 are considered on the borderline of statistical significance.

Why do we reject the null when the test statistic is greater than the critical value?

That is, it entails comparing the observed test statistic to some cutoff value, called the “critical value.” If the test statistic is more extreme than the critical value, then the null hypothesis is rejected in favor of the alternative hypothesis.

What does it mean when null hypothesis is rejected?

After a performing a test, scientists can: Reject the null hypothesis (meaning there is a definite, consequential relationship between the two phenomena), or. Fail to reject the null hypothesis (meaning the test has not identified a consequential relationship between the two phenomena)

What if the p-value is less than the null hypothesis?

Using a binomial test, the p -value is < 0.0001. (Actually, R reports it as < 2.2e-16, which is shorthand for the number in scientific notation, 2.2 x 10 -16, which is 0.00000000000000022, with 15 zeros after the decimal point.) Assuming an alpha of 0.05, since the p -value is less than alpha, we reject the null hypothesis.

What is the R value in statistics?

The ” r value” is a common way to indicate a correlation value. More specifically, it refers to the (sample) Pearson correlation, or Pearson’s r. The “sample” note is to emphasize that you can only claim the correlation for the data you have, and you must be cautious in making larger claims beyond your data.

What is the p-value in statistics?

Recall that the definition of the p -value is: Given the assumption that the null hypothesis is true , the p -value is defined as the probability of obtaining a result equal to or more extreme than what was actually observed in the data.

What happens if the p-value is less than alpha?

If the p -value for the test is less than alpha , we reject the null hypothesis. If the p -value is greater than or equal to alpha , we fail to reject the null hypothesis. For an example of using the p -value for hypothesis testing, imagine you have a coin you will toss 100 times.