FREE TOOL
LinkedIn A/B Test Calculator
Compare two outreach variants and find out whether the difference is real or just noise. Enter each variant's sent and conversion counts for an instant significance verdict.
Variant A
Variant B
Result
Variant B wins
At 95% confidence (p = 0.048). Variant B converts 9.0% higher in absolute terms.
25.0%
Variant A rate
34.0%
Variant B rate
36.0%
Relative lift (B vs A)
How do you know if a LinkedIn A/B test is significant?
Run a two-proportion z-test on the two variants. If the p-value is below 0.05, the difference is significant at 95% confidence and the higher-converting variant is a real winner; above 0.05, the gap could be chance. Test one variable at a time (note, opener, or follow-up) and keep both running until the p-value clears the bar rather than calling it early.
How this is calculated
The calculator computes each variant's conversion rate, then runs a two-proportion z-test (pooled standard error) to get a z-score and a two-tailed p-value.
A p-value below 0.05 is treated as significant at 95% confidence. Conversions can be accepts (for note tests) or replies (for message tests); keep the metric consistent across both variants.
Free to use and cite with attribution (CC BY 4.0). Benchmark figures from The Linked Insider Benchmark.
Frequently asked questions
How do you know if a LinkedIn A/B test is statistically significant?
Compare the two variants with a two-proportion z-test. If the p-value is below 0.05, the difference is significant at 95% confidence and the higher-converting variant is a real winner. Above 0.05, the gap could be noise. This calculator runs that test on your sent and conversion counts automatically.
How much data do I need to A/B test LinkedIn outreach?
It depends on the size of the difference. Big gaps reach significance with a few hundred sends per variant; small gaps need thousands. A good rule is to keep both variants running until the p-value drops below 0.05, rather than calling a winner after a handful of replies.
What is a p-value in an A/B test?
The p-value is the probability of seeing a difference this large by chance if the two variants were actually equal. A p-value of 0.03 means roughly a 3% chance the gap is random. Below 0.05 is the conventional bar for declaring a statistically significant winner.
What should I A/B test in LinkedIn outreach?
Test one variable at a time: the connection note, the opening message, the first follow-up, or the target segment. Measure acceptance rate for note tests and reply rate for message tests. Enter sent and conversion counts for each variant here to see which wins and by how much.
Test variants without the spreadsheet
Reachium runs outreach variants on the verified API and tracks acceptance and reply rates in its analytics, so the winner is clear.
MORE FREE TOOLS
LinkedIn Outreach ROI Calculator
Project accepts, replies, meetings, and pipeline from your inputs, using real acceptance and reply benchmarks.
LinkedIn Connection Limit Checker
Get a recommended daily invite cap for your account, with the data-backed sweet spot and rate-limit warnings.
LinkedIn Acceptance Rate Grader
Enter your sent, accepted, and replied counts to see your rates against the 2026 benchmark bands.
LinkedIn Character Counter
Count characters, find the "see more" cutoff, and score your post against the engagement sweet spot.
LinkedIn Character Limits
Every LinkedIn character limit in one place, with a live counter for each field.
LinkedIn Text Formatter
Turn plain text into bold, italic, or underline that survives the LinkedIn composer.