# BINOM_TEST

The BINOM_TEST node is based on a numpy or scipy function. The description of that function is as follows: Perform a test that the probability of success is p. Note: 'binom_test' is deprecated; it is recommended that 'binomtest' be used instead. This is an exact, two-sided test of the null hypothesis that the probability of success in a Bernoulli experiment is 'p'. Params: k : Scalar int, aka k. The number of successes. n : int The number of trials. This is ignored if x gives both the number of successes and failures. p : float The hypothesized probability of success. 0 <= p <= 1. The default value is p = 0.5. alternative : {'two-sided', 'greater', 'less'} Indicates the alternative hypothesis. The default value is 'two-sided'. Returns: out : DataContainer type Vector with 2 values: statistic and pvalue.
Python Code
from flojoy import flojoy, Vector, Scalar
import scipy.stats

@flojoy
def BINOM_TEST(
k: Scalar,
n: int = 2,
p: float = 0.5,
alternative: str = "two-sided",
) -> Vector:
"""The BINOM_TEST node is based on a numpy or scipy function.

The description of that function is as follows:

Perform a test that the probability of success is p.

Note: 'binom_test' is deprecated; it is recommended that 'binomtest' be used instead.

This is an exact, two-sided test of the null hypothesis that the probability of success in a Bernoulli experiment is 'p'.

Parameters
----------
k : Scalar
int, aka k. The number of successes.
n : int
The number of trials.  This is ignored if x gives both the
number of successes and failures.
p : float, optional
The hypothesized probability of success. 0 <= p <= 1.
The default value is p = 0.5.
alternative : {'two-sided', 'greater', 'less'}, optional
Indicates the alternative hypothesis.
The default value is 'two-sided'.

Returns
-------
DataContainer
type Vector with 2 values: statistic and pvalue.
"""

result = scipy.stats.binomtest(
k=k.c,
n=n,
p=p,
alternative=alternative,
)

result = [result.statistic, result.pvalue]

return Vector(v=result)


Find this Flojoy Block on GitHub