Coefficients having p values less than alpha are significant. For example, if you chose alpha to be 0.05, coefficients having a p value of 0.05 or less would be statistically significant (i.e., you can reject the null hypothesis and say that the coefficient is significantly different from 0).
If your p-value is less than your selected alpha level (typically 0.05), you reject the null hypothesis in favor of the alternative hypothesis. If the p-value is above your alpha value, you fail to reject the null hypothesis.
What if the p-value is less than the significance level α?
If your P value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample gives reasonable evidence to support the alternative hypothesis.
The p-value is greater than alpha. In this case, we fail to reject the null hypothesis. When this happens, we say that the result is not statistically significant. In other words, we are reasonably sure that our observed data can be explained by chance alone.
Should the null hypothesis be rejected when the p-value is less than α?
In any null hypothesis significance test, when the p-value associated with a test statistic is less than the alpha level in the study, the null hypothesis should be rejected. This is equivalent to the case when the test statistic falls inside the rejection region.
The p-value only tells you how likely the data you have observed is to have occurred under the null hypothesis. If the p-value is below your threshold of significance (typically p < 0.05), then you can reject the null hypothesis, but this does not necessarily mean that your alternative hypothesis is true.
If the probability (i.e., p-value) is less than alpha that we would obtain a sample mean this large or larger from the null population, we reject the null hypothesis and conclude that that our sample was drawn from a different population with a sample mean larger than the null mean.
You can reject a null hypothesis when a p-value is less than or equal to your significance level. The p-value represents the measure of the probability that a certain event would have occurred by random chance. You can calculate p-values based on your data by using the assumption that the null hypothesis is true.
What happens if the p-value is less than alpha the level of significance in a two tailed test?
The smaller (closer to 0) the p-value, the stronger is the evidence against the null hypothesis. If the p-value is less than or equal to the specified significance level α, the null hypothesis is rejected. Otherwise, the null hypothesis is not rejected.
When the p-value is less than α then the results are statistically significant?
In statistical hypothesis testing, you reject the null hypothesis when the p-value is less than or equal to the significance level (α) you set before conducting your test. The significance level is the probability of rejecting the null hypothesis when it is true.
05 are considered on the borderline of statistical significance. If the p-value is under . 01, results are considered statistically significant and if it's below . 005 they are considered highly statistically significant.
A p value of 0.06 means that there is a probability of 6% of obtaining that result by chance when the treatment has no real effect. Because we set the significance level at 5%, the null hypothesis should not be rejected.
at the margin of statistical significance (p<0.07) close to being statistically significant (p=0.055) fell just short of statistical significance (p=0.12) just very slightly missed the significance level (p=0.086)
And this is exactly it: When we put it that way, saying that we want the probability (of the null hypothesis being true) — called a p-value — to be less than 5%, we have essentially set the level of significance at 0.05. If we want the probability to be less than 1%, we have set the level of significance at 0.01.
A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance. A non-significant result, leading us not to reject the null hypothesis, is evidence that the null hypothesis is true. Non-significant results are a sign that the study has failed.
How do you know if you should fail to reject the null hypothesis?
If the P-value is less than or equal to the significance level, we reject the null hypothesis and accept the alternative hypothesis instead. If the P-value is greater than the significance level, we say we “fail to reject” the null hypothesis.
When the p-value is less than the significance level, the usual interpretation is that the results are statistically significant, and you reject H 0. For one-way ANOVA, you reject the null hypothesis when there is sufficient evidence to conclude that not all of the means are equal.
A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.
- The P-value approach is more direct in the context of hypothesis testing - if the P-value is less than alpha, we reject the null hypothesis. A p-value below alpha indicates statistical significance, meaning there's enough evidence to reject the null hypothesis, regardless of how much lower the p-value is than alpha.
p-value represents the probability of getting a test statistic as extreme as the one provided by the sample, if the null hypothesis is true. Hence if the p-value is very small ( less than alpha), the null hypothesis being true is not likely and hence reject the null hypothesis.
What happens if p-value is less than significance level?
Given the null hypothesis is true, a p-value is the probability of getting a result as or more extreme than the sample result by random chance alone. If a p-value is lower than our significance level, we reject the null hypothesis. If not, we fail to reject the null hypothesis.
A p-value less than 0.05 is typically considered to be statistically significant, in which case the null hypothesis should be rejected. A p-value greater than 0.05 means that deviation from the null hypothesis is not statistically significant, and the null hypothesis is not rejected.
Reject the null hypothesis when the p-value is less than or equal to your significance level. Your sample data favor the alternative hypothesis, which suggests that the effect exists in the population. For a mnemonic device, remember—when the p-value is low, the null must go!
The p-value obtained from the data is judged against the alpha. If alpha=0.05 and p=0.03, then statistical significance is achieved. If alpha=0.01, and p=0.03, statistical significance is not achieved.
