# prevalencePower

##### 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.

##### 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.

##### See Also

##### Examples

```
# NOT RUN {
### 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 0.7.2, License: GPL (>= 3)*

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