calculate_sample_size: Calculate sample size for desired statistical power in...

In eliobartos/misc: Miscellaneous functions

Description

Calculates needed sample size in AB test for a given baseline, improvement, statistical significance and statistical power. Calculations are based on Center Limit Theorem and sample size is found using binary search over lower_n, upper_n.

Usage

calculate_sample_size(
  baseline,
  improvement,
  significance,
  wanted_power,
  improvement_type = "relative",
  tolerance = 1e-04,
  upper_n = 1e+07,
  lower_n = 1
)

Arguments

baseline	Base rate of success for the control group in range <0,1>
improvement	Relative increase of baseline or absolute number that we want our test to pick up, recognize with given statistical power
significance	Statistical significance, probability of rejecting H0 when it is actually true
wanted_power	Statistical power we want our test to have, probability of rejecting H0 when it is false and H1 is given by baseline and improvement
improvement_type	'relative' or 'absolute'. Is given improvement relative to baseline or given in absolute number.
tolerance	When searching wanted_power using binary search, how close we need to get in order to finish searching. Defaults to 0.00001 = 0.001%.
upper_n	Upper limit for sample size when searching using binary search. If solution is above upper_n we will get a message to increase it. Default value is 1e7 = 10 million.
lower_n	Lower limit for sample size when searching using binary search. Default value is 1.

Value

Integer, estimated sample size needed in order to achieve wanted statistical power.

Author(s)

Elio Bartoš

Examples

calculate_sample_size(
  baseline = 0.3,
  improvement = 0.02,
  significance = 0.05,
  wanted_power = 0.90,
  improvement_type = "absolute")

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eliobartos/misc documentation built on Oct. 8, 2021, 1:10 a.m.