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Sample Size Calculator for Planning AB Tests

Calculate the required sample size for statistical research and experiments, taking into account the significance level and confidence interval.

%20.00%
%15% β€” 25%

Conversion rates in the gray area will not be distinguishable from the baseline.

Sample size:

1,030

per variation

Percent of the time the minimum effect size will be detected, assuming it exists

Percent of the time a difference will be detected, assuming one does NOT exist

Sample Size Calculator for Planning A/B Tests

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The sample size calculator helps determine how many observations are needed to obtain statistically significant results. This is important for planning experiments, research, and marketing tests.

The sample should be large enough for the results to be reliable, but not excessively large to avoid wasting resources. Our tool takes into account the confidence level, margin of error, and expected data variability.

This tool is useful for analysts, researchers, marketers, and anyone who works with statistical data and wants to optimally plan research.

Frequently Asked Questions (FAQ)

Sample size depends on desired confidence level, margin of error, population size, and expected effect size. Larger effects require smaller samples, while smaller effects need larger samples to detect.

Confidence level (usually 95%) indicates how sure you are about your results. Margin of error is the range of uncertainty around your estimate. Higher confidence or lower margin of error requires larger samples.

If unknown, use 50% (0.5) as it gives the most conservative (largest) sample size. If you have prior data or estimates, use those values for more accurate calculations.

For small populations (less than 20 times your sample size), use finite population correction to reduce required sample size. For large populations, treat as infinite.

Different studies (surveys, experiments, comparisons) have different requirements. Specify your study type, primary outcome, and analysis method for accurate sample size calculations.
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Determine Minimum Number of Observations

Allows you to calculate how much data is needed to obtain statistically significant results.

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Takes into Account Confidence Interval and Significance Level

Helps reduce the likelihood of error in experiments and marketing tests.

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Useful for A/B Testing

Optimizes the data collection process, eliminating redundant resources for analysis.