Several factors affect the power
of a statistical test. Some of the factors are under the control
of the experimenter whereas others are not. The following example
will be used to illustrate the various factors.

Sample Size

The
larger the sample size, the higher the power. Since sample size
is typically under an experimenter's control, increasing sample
size is one way to increase power. However, it is sometimes
difficult and/or expensive to use a large sample size.

Standard Deviation

Power is higher when
the standard deviation is small than when it is large. Experimenters
can sometimes control the standard deviation by sampling from
a homogeneous population of subjects or by reducing random measurement
error.

Difference between Hypothesized and True Mean

Naturally, the larger the effect size, the more
likely it is that an experiment would find a significant effect.

Significance Level

There is a tradeoff between the significance level and power:
the more stringent (lower) the significance level, the lower the
power. Power is lower for the 0.01 level than
it is for the 0.05 level. Naturally, the stronger the evidence
needed to reject the null hypothesis, the lower the chance that
the null hypothesis will be rejected.

One- versus Two-Tailed Tests

Power is higher with a one-tailed test
than with a two-tailed test as long as the hypothesized direction
is correct. Power is higher with a one-tailed test
than with a two-tailed test as long as the hypothesized direction is correct.
A one-tailed test at the 0.05 level has the same power as a two-tailed
test at the 0.10 level. A one-tailed test, in effect, raises the
significance level.