Share When reading news articles that cite polling data or survey research, you’ve probably come across terms like “margin of error” and “confidence level.” It’s easy to assume that a smaller margin of error and a higher confidence level mean more reliable results – but what do these numbers actually mean? What does a ±3% margin of error really tell us? Is an 80% confidence level too low to trust?
In this article, we’ll break down what margin of error and confidence level really mean and how to interpret them. We’ll also walk through these concepts using sample survey data from Dataspace.
What Is Margin of Error?
The margin of error tells you how much surve’y results might differ from the actual opinions of the total population. Most surveys don’t include everyone. For example, if you’re researching the opinions of the U.S. population aged 20–39, surveying all 70 million people would be unrealistic. So, researchers select a sample that represents the larger population.
If the sampling process is unbiased, survey results will reflect the overall population’s trends. That’s why we rely on public opinion polls or exit polls in elections. But since it’s not a full census, the results are never 100% exact. The margin of error tells us how far off the results might be.
How to read it
It’s simple: add and subtract the margin of error to your result. For example, if brand awareness is 70% with a margin of error of ±3% (at 80% confidence level), you can expect that if you ran the same survey 100 times, 80 of them would show brand awareness between 67% and 73%.
How to Reduce Margin of Error
The more people you survey, the closer your results will be to the full population—and the smaller the margin of error. But surveying more people means higher cost and longer timelines. On the other hand, if your sample is too small, even one or two responses can skew your results.
It’s important to find a balance. Consider your research objective, timeline, and budget. Here are general guidelines, though these may vary depending on the type of research (e.g., B2B or international surveys):
💡What’s an appropriate sample size?
Statistically, a sample of 30 or more is considered meaningful.
However, for marketing and user research surveys, it’s recommended to have at least 100 respondents per segment (cell) for stable analysis.
For example, if you want to break down results by gender and age group for people in their 20s and 30s, you’ll have four cells:
- 20s male
- 20s female
- 30s male
- 30s female
You should aim for at least 100 respondents per cell, for a total of 400 or more.
What Is Confidence Level?
The confidence level shows how often your survey would get similar results if you ran the same survey over and over. For example, an 80% confidence level means that 80 out of 100 times, the results would fall within the margin of error.
How does it affect margin of error?
Let’s go back to our previous example:
Brand awareness is 70%, with a ±3% margin of error at 80% confidence level. That means 80 times out of 100, the score will be between 67% and 73%.
Now, what happens if you want a 95% confidence level for the same survey? You’d need your estimate to be correct 95 times out of 100, which requires a wider margin of error. So:
- Higher confidence level → Larger margin of error (if sample size stays the same)
- Lower confidence level → Smaller margin of error
This means you’ll only consider differences significant if they’re large enough to stand out at a high confidence level.
Is Higher Always Better?
Not necessarily. A high confidence level might miss small but important differences—especially in competitive markets.
Say your competitor’s brand awareness is 5% higher than yours.
- At 95% confidence, you may see this as an insignificant gap.
- But at 80% confidence, it might be flagged as meaningful—and you can act faster.
So, your chosen confidence level should match your research goals. In fast-moving markets, noticing subtle changes early can be a strategic advantage.
💡How to Choose Your Confidence Level
Confidence level is not automatically calculated—you get to choose it. It reflects how strictly you want to interpret your data.
So what’s appropriate?
| Survey Type | Common Confidence Level |
|---|---|
| Public opinion polls | 95% (high accuracy needed) |
| Marketing and user research | 80% or 90% (more sensitive to small shifts) |
Setting a lower confidence level helps catch small but valuable trends that could otherwise be dismissed as noise.
Real-world Applications through Dataspace
Now that you understand margin of error and confidence level, it’s time to apply them.
In Dataspace, you can change the confidence level and instantly see how it affects your survey results. Below is a sample survey on wireless earphones:
In the results summary view, Dataspace shows how margin of error increases as you raise the confidence level. The confidence level is set to 80% by default and researchers can adjust it to 90% or 95% in the sidebar. As you increase the confidence level, you’ll see the margin of error grow.
Dataspace highlights statistically significant differences automatically for cross-tab analysis:
- Red: Significantly higher
- Blue: Significantly lower
When you adjust confidence from 80% → 90% → 95%, the number of highlighted cells changes. At lower confidence levels, even small differences are treated as meaningful. At higher levels, only large differences stand out.
Learn More by Exploring Real Data
Now that you understand what margin of error and confidence level really mean, why not see them in action? Explore sample survey datasets in Dataspace, adjust the confidence level, and interpret results quickly.
With Dataspace, anyone can analyze data. You don’t need a degree in Statistics or be a spreadsheets or R whiz. Our platform automatically highlights statistically significant results and translates key insights into plain English, so you can focus on what matters most. Spend less time figuring out what the numbers mean, and more time making decisions with confidence.
Start now with our Free Plan and go from analysis to actionable insights.