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Independent trial

As explained in Statistics Every Programmer Needs

An independent trial is a random trial whose outcome does not alter the probabilities of outcomes in other trials.

An independent trial is a repeated random experiment whose outcome probabilities remain unchanged across trials. In a binomial setting, each trial has two possible outcomes, such as success or failure, and the probability of success stays constant. section 3.1.2 section 3.4

Independent trials support binomial probability calculations when these conditions hold:

Computing a binomial probability

For 20 independent trials with a 0.5 success probability, the probability of exactly 7 successes is calculated as C(20, 7) × 0.5⁷ × 0.5¹³, producing 7.39288330078125%. With p = 0.2, SciPy's binom.pmf returns 5.454985048652523%. section 3.1.2

How Statistics Every Programmer Needs covers it

How deep it goes
Defines constant probabilities and demonstrates binomial PMF calculations for fixed trial counts.
Examples
Uses 20-trial distributions with success probabilities of 0.20, 0.50, 0.75, and 0.90, plus an exactly-seven-success calculation.
What you can do after
Apply it via SciPy's binom.pmf or the binomial formula to calculate exact-success probabilities.
How it connects
Connects independent trials to binomial distributions, probability mass functions, counting rules, and normal-looking binomial plots.

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This explainer was generated by Manning's knowledge graph from the text of Statistics Every Programmer Needs. The section links point to the parts of the book it draws on.