# prevalencePower

0th

Percentile

##### Power analysis for establishing a prevalence

This function can be used to establish how many participants are required to establish a prevalence rate with a given margin of error.

Keywords
~kwd1 , ~kwd2
##### Usage
prevalencePower(expectedPrevalence, marginOfError = 0.05, conf.level = 0.95)
##### Arguments
expectedPrevalence
The expected prevalence.
marginOfError
The desired precision.
conf.level
The confidence of the confidence interval.
##### Details

Note that when uncertain as to the expected prevalence, it's better to assume a prevalence closer to 50%. Prevalences closer to 0% or 100% are easier to detect and therefore have more power.

##### Value

The required number of participants.

convert.percentage.to.se

##### Aliases
• prevalencePower
##### Examples
### Required participants for detecting a prevalence of 10%
### with a 95% confidence interval of 10% wide:
prevalencePower(.1);

### Required participants for detecting a prevalence of 10%
### with a 95% confidence interval of 4% wide:
prevalencePower(.1, .02);

### Required participants for detecting a prevalence of 60%
### with a 95% confidence interval of 10% wide:
prevalencePower(.6);

Documentation reproduced from package userfriendlyscience, version 0.5-2, License: GPL (>= 2)

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