What if p-value is less than alpha?
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 happens if the p-value is less than the significance?
If the p-value is lower than a pre-defined number, the null hypothesis is rejected and we claim that the result is statistically significant and that the alternative hypothesis is true. On the other hand, if the result is not statistically significant, we do not reject the null hypothesis.
Is p-value is less than alpha is it significant?
If the p-value is less than or equal to the alpha (p< . 05), then we reject the null hypothesis, and we say the result is statistically significant. If the p-value is greater than alpha (p > . 05), then we fail to reject the null hypothesis, and we say that the result is statistically nonsignificant (n.s.).
What does it mean when you use a 0.05 level of significance alpha level to evaluate statistical results?
The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.
What does not statistically significant mean?
The “layman’s”meaning of not statistically significant is that the strength of relationship or magnitude of difference observed in your SAMPLE, would more likely NOT BE OBSERVED IN the POPULATION your sample purports to represent.
What does it mean that the results are not statistically significant for this study?
This means that the results are considered to be „statistically non-significant‟ if the analysis shows that differences as large as (or larger than) the observed difference would be expected to occur by chance more than one out of twenty times (p > 0.05).
Should we focus on the p-value instead of the alpha level?
Question: Should we focus on the p-value instead of the alpha level? Yes – alpha is arbitrary, while the p-value gives a better representation of the amount of evidence we have to reject the null.
Is a high p-value good or bad?
A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis. Always report the p-value so your readers can draw their own conclusions.
Is the p-value less than or equal to Alpha?
There are two possibilities that emerge: The p-value is less than or equal to alpha. In this case, we reject the null hypothesis. When this happens, we say that the result is statistically significant. In other words, we are reasonably sure that there is something besides chance alone that gave us an observed sample.
How do you know if a p value is statistically significant?
How do you know if a p -value is statistically significant? The level of statistical significance is often expressed as a p -value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p -value less than 0.05 (typically ≤ 0.05) is statistically significant.
When is a result statistically significant at the level of Alpha?
To say that a result is statistically significant at the level alpha just means that the p-value is less than alpha. For instance, for a value of alpha = 0.05, if the p-value is greater than 0.05, then we fail to reject the null hypothesis.
How is the p value related to the null hypothesis?
The p-value assumes the null hypothesis is true and provides the probability of results in excess as the ones observed IF the null hypothesis is true. The p-value is the probability based on the data assuming the null hypothesis is true. The p-value is not the probability of the null hypothesis itself.