What is margin of error and significans testing?

In this article, we’ll explain what margin of error and significance testing mean in market research, why they are important, and how Penetrace applies these concepts to help you interpret results with confidence.

What is Margin of Error?

The margin of error describes how much your survey results might differ from the true value in the population, simply because you are working with a sample of respondents rather than the entire population.

Two things affect the margin of error:

Sample size: Larger samples give smaller margins of error; smaller samples give larger margins of error.
Percentage level: The margin of error is not the same across all percentages. It is largest around 50%, and becomes smaller closer to 0% or 100%.

For example, if 50% of respondents say they are aware of your brand, and the margin of error is ±3%, the true population awareness is likely somewhere between 47% and 53%.

What is Significanse testing?

Significance testing is a statistical method that checks whether a difference between two results is likely to be real or just due to random variation in the sample.

It is closely related to the margin of error, but it’s not as simple as saying “if the difference is bigger than the margin of error, it’s significant.” Why?

The margin of error applies to one result at a time, while significance testing looks at the difference between two results.
The margin of error is not the same across all percentages. It is largest around 50%, and smaller closer to 0% or 100%.

Imagine brand awareness goes from 50% in one wave to 55% in the next:

If each wave has a small sample, that 5-point difference may not be statistically significant.
If each wave has a large sample, the same 5-point difference is much more likely to be significant.

So, whether a change is meaningful depends not only on the size of the difference, but also on the sample size and the percentage level.

How Penetrace Handles It

In Penetrace, we use significance testing to highlight the changes that are most likely to be real—so you can focus on the insights that truly matter, rather than normal sample noise.

All tests are calculated at a 95% confidence level, which is the standard in market research.

By applying this method, Penetrace ensures you get clear guidance on which shifts in your data are meaningful, helping you make decisions with confidence.

Key takeaway

In brand tracking and campaign testing, we want to identify real shifts in consumer attitudes and behavior.

Margin of error helps you understand the level of precision in your data.
Significance testing ensures you don’t over-interpret random fluctuations.

Together, they help you avoid jumping to conclusions and instead focus on real, meaningful changes.

FAQ

What is rolling averages?