How do you explain non-significant results?
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 you discuss non-significant results?
In any research as important is to detect significant differences in a particular comparison, as it is the finding of no statistically significant results, and therefore should be discussed. For example, a non-significant difference can be practical and useful its application to society.
What does it mean when there is no significant difference?
The statement ‘there is no significant difference between groups’, which is often seen in the orthopaedic literature, may only mean ‘there is no statistically detected difference between the groups in our study’.
How do you report non-significant regression results?
As for reporting non-significant values, you report them in the same way as significant. Predictor x was found to be significant (B =, SE=, p=). Predictor z was found to not be significant (B =, SE=, p=).
How do you report non-significant interactions?
When reporting non-significant results, the p-value is generally reported as the a posteriori probability of the test-statistic. For example: t(28) = 1.10, SEM = 28.95, p = . 268.
What does significant and not significant mean in statistics?
A result of an experiment is said to have statistical significance, or be statistically significant, if it is likely not caused by chance for a given statistical significance level. It also means that there is a 5% chance that you could be wrong.
What does a non-significant p-value mean?
These are as follows: if the P value is 0.05, the null hypothesis has a 5% chance of being true; a nonsignificant P value means that (for example) there is no difference between groups; a statistically significant finding (P is below a predetermined threshold) is clinically important; studies that yield P values on …
What does no significance mean?
: not significant: such as. a : insignificant. b : meaningless. c : having or yielding a value lying within limits between which variation is attributed to chance a nonsignificant statistical test.
What does it mean if a regression is not significant?
A low p-value (< 0.05) indicates that you can reject the null hypothesis. However, the p-value for East (0.092) is greater than the common alpha level of 0.05, which indicates that it is not statistically significant. Typically, you use the coefficient p-values to determine which terms to keep in the regression model.
How do you interpret a non-significant moderation?
When there is no Significance interaction it means there is no moderation or that moderator does not play any interaction on the variables in question. However this doesn’t mean in practice there isn’t any interaction.
What is the difference between significant and non-significant results?
If she does another study, she might get a significant result, which would show her one way or another, but with a non-significant result, she cannot make any inferences.
Is a non-significant result a failure of the study?
Since the purpose of research is to make generalizations about the real world, some people see a non-significant result as a failure of the study. It’s true that you can’t make generalizations based on a non-significant result. But remember that it also means that you can’t make generalizations about the opposite of the hypothesis, either.
Can you pass a dissertation with non-significant results?
Next, this does NOT necessarily mean that your study failed or that you need to do something to “fix” your results. Rest assured, your dissertation committee will not (or at least SHOULD not) refuse to pass you for having non-significant results. They will not dangle your degree over your head until you give them a p -value less than .05.
How to deal with non-significant results?
There is a lot of literature dealing with non-significant (“negative”) results. What you need is to protect against type II error (i.e. from accepting a false null hypothesis). Try testing for equivalence. For example, if you have access to Stata, use the command “equim” developed by Richard Goldstein.