The **power** command provides many built-in methods, but sometimes, you may want to compute sample size or power yourself. For example,
you may need to do this by simulation, or you may want to use a method
that is not available in any software package. **power** makes it
easy for you to add your own method. All you need to do is to write a
program that computes sample size, power, or effect size, and the
**power** command will do the rest for you. It will deal with the
support of multiple values in options and with automatic generation of
graphs and tables of results.

Suppose you want to add the method called **mymethod** to the
**power** command. Just follow these three steps:

- Create a program that computes sample size, power, or
effect size and follows
**power**'s naming convention:**power_cmd_mymethod**. - Store results following
**power**'s simple naming conventions for results. For example, store the value of power in**r(power)**, the value of sample size in**r(N)**, and so on. - Place your program
**power_cmd_mymethod**where Stata can find it.

To show how easy this all is, we'll write a program to
compute power for a one-sample *z* test given sample size,
standardized difference, and significance level. For simplicity, we
assume a two-sided test.

We will call our new method **myztest**.

program power_cmd_myztest, rclass version 16.0 // parse options syntax , n(integer) sample size STDDiff(real) standardized diff. Alpha(string) significance level // compute power tempname power scalar `power' = normal(`stddiff'*sqrt(`n') - invnormal(1-`alpha'/2)) // return results return scalar power = `power' return scalar N = `n' return scalar alpha = `alpha' return scalar stddiff = `stddiff' end

The computation in this program takes only one line, but it could be as complicated as we like. It could even involve simulation to compute the power.

With our program in hand, we can type

.power myztest, n(20) stddiff(1) alpha(.05)

**power** will find our program, supply it with the
**n(20)**, **stddiff(1)**, and **alpha(.05)** options, and use its
returned results to produce

.power myztest, n(20) stddiff(1) alpha(.05)Estimated power Two-sided test

alpha power N | |||

.05 .994 20 | |||

That wasn't too impressive. Our program did all the work.

But what if we supplied **power** with a list of sample sizes?

.power myztest, n(10 15 20 25) stddiff(1)Estimated power Two-sided test

alpha power N | |||

.05 .8854 10 | |||

.05 .9721 15 | |||

.05 .994 20 | |||

.05 .9988 25 | |||

**power** has taken our list of sample sizes and computed
powers for all of them—even though our program
could compute only a single power!

Moreover, we can use **power**'s standard **table()** option
to control exactly how that table looks. **power** also
has hooks that let our program determine how the
columns are labeled and how the table appears.

We can supply both sample sizes and significance levels and request a graph instead of a table:

.power myztest, n(10(1)20) alpha(.05 .10 .25) stddiff(1) graph

We can even request that the graph show *α*
on the *x* axis with separate plots for each sample size.

.power myztest, n(10(2)20) alpha(.05 .10 .25) stddiff(1) graph(xdim(alpha))

Now all this may just make it worth writing more complicated programs to compute power for more complicated tests and comparisons.

Learn more about Stata's power, precision, and sample-size features.

Read more about adding your own power and sample-size methods in the
*Stata Power, Precision, and Sample-Size Reference Manual*;
see [PSS-2] *power usermethod*.