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Sequential Sampling Calculator for A/B Tests

Sequential sampling helps make real-time decisions. Our tool is suitable for running sequential A/B tests.

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%15% β€” 25%

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

Percent of the time the minimum effect will be detected if it exists

Percent of the time a difference will be detected if it does NOT exist

Sequential Sampling Calculator for A/B Testing

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The sequential sampling calculator helps determine the optimal sample size for sequential A/B testing. This tool uses statistical methods to calculate the minimum number of observations needed for reliable results.

Sequential testing allows you to stop the experiment earlier when statistical significance is reached, saving time and resources. The tool takes into account baseline conversion, minimum detectable effect, and statistical power.

This calculator is especially useful for marketers, data analysts, and A/B testing specialists who need to optimize the experimentation process and get quick results.

Frequently Asked Questions (FAQ)

Sequential sampling is a statistical method that allows you to stop an experiment early when statistical significance is reached. This saves time and resources compared to a fixed sample size.

The calculator uses statistical methods to calculate the minimum sample size based on baseline conversion, minimum detectable effect, statistical power, and significance level. It shows when the experiment can be stopped.

Absolute effect is the difference in percentage points (e.g., a 5% increase in conversion). Relative effect is the percentage change from the baseline (e.g., a 25% increase from a 20% baseline conversion).

Statistical power (60-95%) determines the probability of detecting an effect if it exists. Significance level (1-10%) determines the probability of a false positive. Higher power requires a larger sample size.

Sequential testing is ideal for A/B tests with high observation costs, where saving resources is important. It's also useful for quickly obtaining results in marketing campaigns.
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Sample Size Calculation for Sequential Testing

Calculates the optimal sample size based on baseline conversion, minimum detectable effect, and statistical parameters for sequential testing.

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Statistical Power and Significance Level Consideration

Allows you to set statistical power (60-95%) and significance level (1-10%) for accurate test results.

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Resource Optimization for A/B Tests

Helps save resources by allowing you to stop the experiment earlier when statistical significance is achieved.