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When supplied non-negative integers `x` (number of successes in a Bernoulli experiment) and `n` (total number of trials), the function computes an exact test for the success probability in a Bernoulli experiment. Alternatively, `x` may be a two-element array containing the number of successes and failures, respectively.
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```javascript
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var out =binomTest( 550, 1000 );
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var out =binomialTest( 550, 1000 );
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/* returns
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{
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'rejected': true,
@@ -46,7 +46,7 @@ var out = binomTest( 550, 1000 );
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}
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*/
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out =binomTest( [ 550, 450 ] );
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out =binomialTest( [ 550, 450 ] );
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/* returns
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{
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'rejected': true,
@@ -86,7 +86,7 @@ The function accepts the following `options`:
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By default, the hypothesis test is carried out at a significance level of `0.05`. To choose a different significance level, set the `alpha` option.
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```javascript
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var out =binomTest( 59, 100, {
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var out =binomialTest( 59, 100, {
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'alpha':0.1
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});
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/* returns
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By default, a two-sided test is performed. To perform either of the one-sided tests, set the `alternative` option to `less` or `greater`.
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```javascript
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out =binomTest( 550, 1000, {
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out =binomialTest( 550, 1000, {
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'alternative':'greater'
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});
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table =out.print();
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Test Decision: Reject null in favor of alternative at 5% significance level
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*/
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out =binomTest( 550, 1000, {
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out =binomialTest( 550, 1000, {
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'alternative':'less'
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});
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table =out.print();
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To test whether the success probability in the population is equal to some other value than `0.5`, set the `p` option.
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