What if there is no significant difference?

What if there is no significant difference?

Perhaps the two groups overlap too much, or there just aren’t enough people in the two groups to establish a significant difference; when the researcher fails to find a significant difference, only one conclusion is possible: “all possibilities remain.” In other words, failure to find a significant difference means …

What does it mean if there is no statistical significance?

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).

How do you report not significant results?

A more appropriate way to report non-significant results is to report the observed differences (the effect size) along with the p-value and then carefully highlight which results were predicted to be different.

What does it mean when the result is not significant?

Non-significance in statistics means that the null hypothesis cannot be rejected. In laymen’s terms, this usually means that we do not have statistical evidence that the difference in groups is not due to chance.

What does it mean if results are statistically significant?

What is statistical significance? “Statistical significance helps quantify whether a result is likely due to chance or to some factor of interest,” says Redman. When a finding is significant, it simply means you can feel confident that’s it real, not that you just got lucky (or unlucky) in choosing the sample.

How do you report statistically significant results?

All statistical symbols (sample statistics) that are not Greek letters should be italicized (M, SD, t, p, etc.). When reporting a significant difference between two conditions, indicate the direction of this difference, i.e. which condition was more/less/higher/lower than the other condition(s).

How is sample size related to statistical significance?

Using the statistical test of equal proportions again, we find that the result is statistically significant at the 5% significance level. Increasing our sample size has increased the power that we have to detect the difference in the proportion of men and women that own a smartphone in the UK.

What happens when your sample size is inadequate?

When your sample size is inadequate for the alpha level and analyses you have chosen, your study will have reduced statistical power, which is the ability to find a statistical effect in your sample if the effect exists in the population. But do not fret!

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…

Why do you need a larger sample size for margin of error?

A narrower margin of error requires a larger sample size. Confidence level – This conveys the amount of uncertainty associated with an estimate. It is the chance that the confidence interval (margin of error around the estimate) will contain the true value that you are trying to estimate. A higher confidence level requires a larger sample size.