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When a result is not statistically significant the correct decision is to?
decide that if a result is not significant, the null hypothesis is shown to be true.
What is the minimum result typically considered statistically significant?
A p-value of 5% or lower is often considered to be statistically significant.
Do you reject if statistically significant?
When your p-value is less than or equal to your significance level, you reject the null hypothesis. Your results are statistically significant. When your p-value is greater than your significance level, you fail to reject the null hypothesis.
How should you interpret a decision that rejects the null hypothesis?
Interpret the decision in the context of the original claim. If the claim is the null hypothesis and H₀ is rejected, then there is enough evidence to reject the claim. If H₀ is not rejected, then there is not enough evidence to reject the claim.
What happens when your results are not significant?
They might be disappointed. They might be worried about how they are going to explain their results. They might panic and start furiously looking for ways to “fix” their study. Whatever your level of concern may be, here are a few things to keep in mind…
Which is the correct value for statistical significance?
A p-value, or probability value, is a number describing how likely it is that your data would have occurred by random chance (i.e. that the null hypothesis is true). The level of statistical significance is often expressed as a p -value between 0 and 1.
Is it OK to cnsider a statistically insignificant result?
If you think that p = 0.1 is enough to “claim a trend”, then you do cnsider your data as significant. That’s perfectly fine. The problem here is that it’s likely that not all reviewers will understand that and outright reject your paper.
Can a statistically significant result prove the null hypothesis?
This means we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis, we can only reject the null or fail to reject it. A statistically significant result cannot prove that a research hypothesis is correct (as this implies 100% certainty).