Significant difference calculator

Welcome to our free significance calculator – a statistical tool you can use to test whether the difference between two values is significantly different!

In a survey, statistical significance is mainly used to identify how confident you can be in the differences you find between two groups. For example, if 70% of group A said they have worked from home during the pandemic, compared to 50% of Group B, you may want to know if this is a significant finding.

Our calculator allows you to find this out simply by entering the sample size (number of respondents) of each independent group in the boxes below, along with the percentages of interest, from each group, that you are looking to compare.

For more information on this topic, you can also read our article “What makes a good, robust, and useable research sample?

Significant Difference Calculator

Confidence level
Group A
Group B
The calculation is performed automatically when one of the values above are changed.

The default confidence level that is applied within the calculator is 95%. What this means is that you can be 95% confident that the differences seen here are real if your result is considered significant. If your result is not significant, you may need to look at reducing the confidence level, increasing the sample size of your groups, or comparing percentages with a larger difference. Feel free to have a play around with the different options!

The lower the confidence level you choose (e.g., 90% or 80%), the more likely your difference will be significant; however, this will also reduce the likelihood that your difference is real.

Conversely, if you increase the sample size within your survey, this will decrease the margin of error within the results, and therefore increase confidence in your data. What this means is that the larger the sample size of the groups you are comparing, the smaller the difference must be between the percentages to be considered ‘significant’.

For more information on confidence levels and intervals, see our blog: What are confidence levels and intervals?

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