Binomial Probability
Calculator

Inputs

Probability
0.250282

Results

Probability
0.250282
Probability (%)
25.028228
Number of combinations
120

Statistical results

Probability0.250282
Probability (%)25.028228
Number of combinations120

formula-map diagram

Probability
0.250282
Probability (%)
25.028228
Number of combinations
120

Statistical relationship

Formula

P(X = k) = C(n, k) × p^k × (1 − p)^(n − k)

= 0.25028228759766

Note

This is a simplified model: it applies the displayed standard formula to the summary values you entered and assumes their underlying conditions (independence, normality, correct sampling) hold. It does not analyse a real data set. Check the assumptions before relying on the result.

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Frequently asked questions

What does a binomial probability calculation tell you?+

It gives the probability of getting exactly k successes in n independent trials, each with the same probability p of success. For example, the probability of flipping exactly 6 heads in 10 fair coin flips.

What conditions must be met to use the binomial formula?+

There must be a fixed number of trials, each trial must be independent, each trial has only two outcomes (success or failure), and the probability of success stays constant across all trials. If the probability changes between trials or trials aren't independent, the binomial model doesn't apply.

How is the formula constructed?+

It's C(n,k) × p^k × (1−p)^(n−k), where C(n,k) counts the number of ways to arrange k successes among n trials, p^k is the probability of those successes, and (1−p)^(n−k) is the probability of the remaining failures.

What's the difference between exact probability and cumulative probability?+

The core binomial formula gives the probability of exactly k successes, like exactly 3 heads. A cumulative probability, by contrast, sums that formula across a range, such as the probability of 3 or fewer heads, or of at least 5 heads.

Why does the probability sometimes surprise people for 'expected' outcomes?+

The single most likely outcome (like exactly 5 heads in 10 flips) often still has a fairly low probability on its own, because there are many other possible outcomes splitting the remaining probability. People often overestimate how likely the 'expected' or average result actually is.