Bernoulli Trial Calculator
Enter a success probability p to describe a Bernoulli trial.
Result
How it is calculated
A Bernoulli trial has two outcomes: success with probability p and failure with probability 1 − p. Counting success as 1 and failure as 0, the mean is E = p, the variance is σ² = p·(1 − p) and the standard deviation is σ = √(p·(1 − p)). Enter p as a probability from 0 to 1 inclusive, so 25 % is 0.25, not 25; anything outside that range, or a blank or non-numeric entry, gives no result. Outputs show 2 decimals, but a small non-zero value keeps extra digits so that p = 0.001 reads a mean of 0.0010 and a variance of 0.00100 rather than 0.00. The variance is largest at p = 0.5, where it is 0.25.
Worked example
A free-throw shooter scores with probability p = 0.3. The mean, counting a make as 1, equals p, so it is 0.30. The variance is 0.3 × (1 − 0.3) = 0.3 × 0.7 = 0.21. The standard deviation is √0.21 ≈ 0.458, shown as 0.46. For 10 independent shots the expected number of makes would be 10 × 0.3 = 3.
Common mistakes
- Entering a percentage such as 30 instead of 0.3. Only 0 to 1 is accepted, so 30 shows no result and the 30 % case is 0.3.
- Reading the mean as a count of successes. For one trial it is the probability itself; for n trials you multiply by n.
- Using the result for several trials. The standard deviation of a total of n trials is √(n·p·(1 − p)), not n times this value.
- Expecting the variance to grow with p. It peaks at p = 0.5 and falls toward 0 near both ends, so p = 0.1 and p = 0.9 have equal variance.
- Reading the standard deviation as tiny for a rare event. For p = 0.001 the variance is 0.00100 but the standard deviation is about 0.03, far larger than p, because the square root of a small number is much bigger than the number itself.
About this calculator
A Bernoulli trial is a single random experiment with exactly two possible outcomes — success or failure — where success happens with probability p and failure with probability 1 − p, named after the 17th-century mathematician Jacob Bernoulli. For a single trial, the mean (expected value) is simply p, the variance is p(1 − p), and the standard deviation is the square root of that variance, √(p(1 − p)).
Bernoulli trials are the basic building block of the binomial distribution: running n independent Bernoulli trials with the same success probability and counting the total number of successes is exactly what the binomial distribution models. This makes the Bernoulli trial the underlying model behind coin flips, pass/fail quality inspections, click-or-no-click A/B testing results, and any yes/no outcome that statisticians want to analyze probabilistically.
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