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Margin of Error Calculator: How Precise Is Your Survey, Really?

Enter your sample size and confidence level to get your margin of error instantly, or flip it around and find the sample size you need.

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Margin of error
+/- 4.90%

With 400 responses at 95% confidence, your true value is within +/- 4.9 percentage points of what you measured.

Reverse: sample size you need

Required sample size
385

You need 385 responses for a +/- 5% margin of error at 95% confidence.

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Margin of error is the plus-or-minus number that tells you how far your survey result can stray from the truth. If 60% of 400 respondents prefer your new landing page and your margin of error is +/- 4.9%, the real figure for your whole audience is very likely somewhere between 55.1% and 64.9%. The smaller the margin, the more precise your result, and the only reliable way to shrink it is to collect more responses. The calculator above gives you the number in both directions: the margin of error for a sample you already have, or the sample size you need to hit a target margin.

The formula, decoded

The standard formula is MoE = z x sqrt(p(1-p)/n). Three ingredients do all the work. The z-score comes from your confidence level: 1.645 for 90%, 1.96 for 95%, 2.576 for 99%. Higher confidence means a wider margin, because you are asking for a stronger guarantee. The proportion p is the percentage you expect to measure; if you have no idea, use 50%, because p(1-p) is largest at 50% and gives you the most conservative (widest) margin. The sample size n sits under a square root, which is why precision gets expensive: cutting your margin of error in half requires four times as many responses, not twice as many.

If you survey a small, known population, the finite population correction tightens the margin. It barely matters until your sample is a meaningful slice of the whole group, which is why the population field is optional.

Quick reference table

At 95% confidence with p = 50%, the workhorse settings for most surveys, the numbers look like this:

Sample sizeMargin of error
100+/- 9.8%
200+/- 6.9%
400+/- 4.9%
1,000+/- 3.1%
2,000+/- 2.2%

Notice the diminishing returns: going from 100 to 400 responses buys you 4.9 points of precision, but going from 1,000 to 2,000 buys you less than one. For most marketing surveys, 400 to 1,000 responses is the sweet spot between cost and credibility.

The same statistics decide whether an A/B test winner or a month-over-month change in your conversion rate calculator numbers is a real difference or just noise. Before you act on any percentage, check that the gap between two results is bigger than their margins of error. And if you want to know which numbers on your own site are worth acting on, request a free SEO audit and we will walk you through them with real data.

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FAQ

Margin of Error Calculator: questions, answered

What is a good margin of error?
For most surveys, 3% to 5% at 95% confidence is the accepted standard; that takes roughly 400 to 1,100 responses. Quick polls and informal customer feedback can tolerate up to 10%, while high-stakes research like political polling usually targets 3% or less.
How do I reduce the margin of error?
Collect more responses; it is the only lever that reliably works. Because sample size sits under a square root, halving your margin of error takes four times the sample. You can also accept a lower confidence level (90% instead of 95%), but that trades certainty for the appearance of precision.
What does 95% confidence mean?
It means that if you repeated the same survey 100 times, about 95 of those surveys would produce a result within the stated margin of error of the true value. It does not mean your specific result has a 95% chance of being exactly right; it describes how reliable the method is over many repetitions.
Does population size matter?
Barely, in most cases. Once the population is more than about 20 times your sample size, the correction changes almost nothing: surveying 400 people gives nearly the same margin whether the population is 10,000 or 10 million. It only matters when you sample a big share of a small group, like 100 of your 300 customers.
Why use 50% as the proportion?
Because 50% is the worst case: the term p(1-p) in the formula peaks when p is 0.5, producing the widest possible margin of error. Using it guarantees your stated margin is safe no matter what result comes back. If past data tells you the real figure is near 10% or 90%, plug that in and your margin shrinks.

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