Research methodology series

Sample size and power

calculations made simple
Evie McCrum-Gardner
Background: Sample size estimation is an important concern for researchers as guidelines
must be adhered to for ethics committees, grant applications and publications. Studies may be
underpowered (too few participants) or overpowered (too many participants) so it is important to
achieve the correct balance.
Content: The process of sample size calculations, including relevant definitions, is explained and clear
examples for different study designs are provided for illustration. A range of software packages and
websites are discussed and evaluated.
Conclusions: The information regarding sample sizes and statistical programs should be useful for
researchers in order to perform sample size calculations for research projects.
Key words: n minimal clinically important difference n power n sample size n significance level n type I error 
n type II error
Submitted 10 July 2009, sent back for revisions 23 September 2009; accepted for publication following double-blind peer review
2 November 2009

S
Evie McCrum-Gardner
is Lecturer in Health
Statistics, Schools of
Nursing and Health
Sciences, Health and
Rehabilitation Sciences
Research Institute,
University of Ulster,
Newtownabbey,
Northern Ireland
Correspondence to:
E McCrum-Gardner
Email: ee.gardner@
ulster.ac.uk

10

ample size estimation is an important concern for researchers undertaking research

projects, but is often misunderstood or even
ignored completely. Guidelines must be
adhered to for ethics committees, grant applications
and publications. Studies may be underpowered (too
few participants) or overpowered (too many participants) so it is important to achieve the correct balance. The aim of this article is to explain the process
of sample size calculations, including the purpose,
relevant definitions and clear examples for different study designs. It will also discuss and evaluate
a range of software packages and websites that are
currently accessible, but which were not available at
the time of earlier publications (e.g. Florey, 1993).
It is not assumed that the reader has previous
experience of statistical tests. However, the choice
of the correct statistical test is vitally important at
the statistical analysis stage. When choosing the
appropriate statistical test, the first step is to decide
what scale of measurement your data is, as this will
affect your decision (nominal, e.g. gender; ordinal,
e.g. Likert scale; interval/ratio, e.g. weight). The
next stage is to consider the analysis required for
example, are you comparing independent or paired
groups? If an incorrect test is used, then invalid
results and misleading conclusions may be drawn
from the study. This is described in detail elsewhere
(e.g. McCrum-Gardner, 2008).

Research Ethics Committees

Guidelines
Guidance on sample size by the Central Office
for Research Ethics Committees (COREC) (2007)
requires that the number should be sufficient to
achieve worthwhile results, but should not be so
high as to involve unnecessary recruitment and
burdens for participants.

Sample Size Calculation

Sample size is a function of three factors the significance level, power, and magnitude of the difference (effect size). These three factors are defined
below with further definitions also provided by
Devane et al (2004). Before explaining these factors, hypothesis testing is described.

Hypothesis testing
Researchers begin with a research hypothesis, for
example, that treatment A is better than treatment
B. Hypothesis testing involves expressing this as
a null hypothesis and performing the appropriate statistical test to investigate whether the null
hypothesis can be accepted/rejected. An example
of a null hypothesis is: there is no difference (in
the mean outcome measure) between treatments
A and B. The researcher wants to be able to reject

International Journal of Therapy and Rehabilitation, January 2010, Vol 17, No 1

the null hypothesis and to show that differences in

the treatment outcomes are not a result of chance,
i.e. that the alternative hypothesis is that there is a
difference between the two treatments.

Significance level (P value)

Significance level is the probability cut-off (usually 0.05 or 5%) used. It is chosen in advance of
performing the test, and the cut-off level depends on
how much safeguard is required against accidentally
rejecting the null hypothesis when it is in fact true.

Power
Power is the probability of rejecting the null
hypothesis when the alternative hypothesis is
true. It measures the ability of a test to reject the
null hypothesis when it should be rejected. At
a given significance level, the power of the test
is increased by having a larger sample size. The
minimum accepted level is considered to be 80%,
which means there is an eight in ten chance of
detecting a difference of the specified effect size.

Type I and type II errors

Performing the appropriate statistical test (McCrumGardner, 2008) will result in either rejecting or not
rejecting the null hypothesis. The definitions of type
I and II errors are given in Table 1.
Type I errors occur if the null hypothesis is
rejected when it is true. From the definition of the
significance level, this will occur one in 20 times if
the test is at the 5% significance level. Probability
of a type I error is therefore the significance level
of the test, and is denoted by alpha ().
Type II errors occur if the null hypothesis is not
rejected when the alternative hypothesis is true.
Probability of a type II error is denoted by beta
(). The power of a test is the probability of not
making a type II error and is therefore 1-.
Significance and power have accepted levels or
minimum criterion levels in research. However,
effect size, which is also a factor in sample size
estimation, is more problematic.

Effect size
The effect size quantifies the difference between
two or more groups. It is a measure of the difference in the outcomes of the experimental
and control groups, i.e. a measure of the effectiveness of the treatment. Cunningham and
McCrum-Gardner (2007) provide formulae for
effect size in different situations, but sample
size software can be used, which requests the
required information (e.g. absolute mean difference and standard deviation) and performs
the calculations. The effect size can often be esti-

Table 1.
Definition of type I and type II errors

Null hypothesis
rejected

Null hypothesis
not rejected

Null hypothesis true

Type I error

Alternative hypothesis true

Type II error

mated from previous publications and based on

the scientific knowledge of the researcher.
Further definitions with regard to clinical significance, clinically important differences, the primary outcome measure, and response rate are
outlined below.

Clinical Versus Statistical

Significance
Just because a result is statistically significant, this
does not mean it is substantive in effect. For example, two treatments could be statistically significantly different, but their clinical effects may be so
small as to be unimportant.

Minimal clinically important

difference (MCID)
The MCID can be defined as the smallest meaningful change score or the smallest (absolute)
difference in score which patients perceive as beneficial, and which would mandate, in the absence
of troublesome side-effects and excessive cost, a
change in the patients management. Therefore,
differences in scores smaller than the MCID are
considered not important independent of their statistical significance (Ruperto, 2007).
It has also been described as a much sought
after, but elusive figure. The MCID for many outcome measures is difficult to estimate and yet, if
known, makes sample size calculations easier and
more accurate.

Primary Outcome Measure

Sample estimation size should be based on the primary outcome measure, but if there is more than
one outcome then the largest sample size should
be chosen so that all the outcome measures are
fully powered.

Response Rate
After the sample size has been calculated, it will
need to be increased depending on the expected
response rate. This can be estimated from previous
publications or a pilot study.

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Research methodology series

Table 2.
Website addresses for software packages
Software
package Website address
Minitab www.minitab.com/en-GB/support/downloads/
PS biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize
GPower www.psycho.uni-duesseldorf.de/aap/projects/gpower/
Epi-info www.cdc.gov/epiinfo/downloads.htm

A selection of software packages and websites

which can be used to perform the calculations are
described below.

Software
There are many software packages for performing
sample size/power calculations. Four useful ones
are described in this article:
n Minitab
n PS
n GPower
n Epi-info/StatCalc
Minitab is simple to use for a beginner but has a
limited range of study designs; it necessitates purchasing a licence, although a 30-day free trial is available (see Table 2 for list of website addresses). PS,
GPower and Epi-info can be downloaded for free.
GPower is more complicated to use for a novice, but does cover a wider range of study designs.
GPower is useful if using effect sizes (small/mod-

erate/large as discussed in Cunningham (2007))

rather than absolute values, but can be complicated if using absolute values to calculate the
effect size.
PS is a good choice of software, although the
use of mathematical symbols can make it appear
more complicated and inaccessible. However, it
has a help facility to provide definitions. The correct format for inputs in PS is not obvious power
should be input as e.g. 0.8 (for 80% power) and
alpha level as e.g. 0.05. It also covers a wide range
of study designs including survival analysis, linear
regression, case-control and cohort studies, as well
as the more commonly used designs in the examples given above. The most up-to-date version (V3)
creates a very useful text description based on the
input values, which could be copied/pasted in to a
publication or grant application.
Epi-info is useful for population surveys, casecontrol and cohort studies.
In summary, PS is probably the best choice of
software, as it covers the most commonly used
study designs, is relatively easy to use and importantly, is easy and free to download. However, the
user may discover his/her own personal preference.

Websites
There are many websites which can be used to
perform sample size/power calculations. Some

Figure 1. Using PS software for the sample size calculation for two independent groups

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Figure 2. Using PS software to produce a graph

useful ones are described below. The study designs

covered vary from website to website.
The Statpages website (http://statpages.
org/#Power) has a very wide range of options,
although the reader should be aware that
some links have not been updated and no
longer work.
Russ Lenths website (http://tinyurl.com/
yhrr695) covers a range of study designs, and
allows the user to adjust the levels of power, mean
difference etc, by dragging the cursor along a continuum. However, this can sometimes be awkward,
depending on the range of values required. Lenths
(2001) article provides some practical guidelines
for sample size calculations using this website.
The Obstetrics and Gynaecology site of the
Chinese University of Hong Kong (http://tinyurl.
com/yep8bfz) has a statistics toolbox, which
includes a sample size menu. It includes a range
of study designs including population surveys,
equivalence studies and ordinal data, as well as
the commonly-used designs in the examples given
above. It also includes examples to facilitate ease
of use.
Power calculations for population surveys can be
performed using the Raosoft website (http://www.
raosoft.com/samplesize.html).
Overall, the Obstetrics and Gynaecology site
of the Chinese University of Hong Kong is a par-

ticularly good choice as it covers a range of study

designs and is relatively easy to use.

Examples
The examples below demonstrate what information is required for the sample size calculation
for a range of study designs. To aid understanding they also demonstrate the increase/decrease
in sample size when factors are modified, e.g.
increasing the mean difference, decreasing the
standard deviation, and increasing the power
level. The PS software has been used for these
examples, but alternatively, the other software packages or websites described above
could be used. More information about the statistical tests mentioned can be obtained from
McCrum-Gardner (2008).
The independent samples t-test is used to compare sample means from two independent groups
for an intervalscale variable when the distribution
is approximately normal; the paired t-test is used
to compare two sample means for an intervalscale
variable where there is a one-to-one correspondence (or pairing) between the samples and the distribution of within-pair differences is approximately
normal; the chi-square (2) test is used to compare
proportions between two or more independent
groups or investigate whether there is any associa-

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Research methodology series

tion between two nominal-scale variables. Sample
size calculations are given below for each of these
three study designs.

Example 1 two independent groups

(independent t-test)
A proposed study wishes to investigate the effects
of a new hypertensive drug (experimental group)
compared to a conventional treatment (control
group). Previous studies show that the minimum
clinically important difference is 15mmHg and that
the pooled standard deviation (SD) is 20mmHg.
Using the PS software (Figure 1) it is estimated that 29 subjects would be needed in each
of the control and experimental groups ( level
P=0.05, power 80%) in order to detect a statistically significant difference in mean blood pressure
between the two groups (if it exists). Increasing
the mean difference to 20mmHg would require 17
participants per group, i.e. a substantial decrease.
Decreasing the SD to 10mmHg would require
only five participants in each group.

Example 2 two proportions

(chi-square test)
A health promotion intervention to reduce smoking
is going to be introduced in a country with a high
prevalence of smoking. The current smoking rate
in a recently published report is 65% and it is estimated that the intervention will reduce the smoking
levels by 30%. A sample size of 42 in each group
is required (P=0.05, power 80%). Increasing the
power level to 90% requires 56 per group.

Example 3 two paired groups

(paired t-test)
Is there evidence that clofibrate changes the mean
cholesterol level? Cholesterol is to be measured
before and after receiving clofibrate. From previous
studies, a mean difference of 40mg/dl is deemed
clinically significant, with standard deviation of 50.
A sample size of 14 is required (P=0.05, power
80%), P=0.01 requires 21 subjects, i.e. an increase.

Key points
n Sample size estimation is a significant concern for researchers as guidelines must
be adhered to for ethics committees, grant applications and publications.
n Studies may be underpowered (too few participants), or overpowered (too
many participants) so it is important to achieve the correct balance.
n Relevant definitions of power, significance level, effect size etc. are provided
in order to understand the process of performing sample size calculations.
n Clear examples of sample size calculations for three commonly-used study
designs are provided for illustration and to aid understanding.
n A range of software packages and websites are available to perform sample
size calculations and a selection of them are discussed and evaluated.

14

Post-hoc Power Calculations

An a priori analysis calculates sample sizes for
given effect sizes, alpha levels and power values. A
post-hoc/retrospective analysis computes power values for given sample sizes, effect sizes, and alpha levels. Both GPower and PS can perform these analyses.
The previously mentioned cholesterol study
(example 3) was carried out but as a result of the
poor response/dropout rate, only 12 patients were
recruited. A mean difference of 50 and SD of
60mg/dl were found. What is the power for this
study? Carrying out a retrospective analysis indicates that the power is 75% i.e. the study is under
powered as it is less than 80%.

Graphical Presentation
Both PS and GPower can produce graphs to explore
the relationships between power, sample size and
effect size, e.g. the difference in population means.
It is often helpful to hold one of these variables constant, and plot the other two against each other. For
example, a plot of difference in means against sample size for the two treatment groups in example 1 is
shown in Figure 2.

Conclusions
The reader must be aware that the choice of the correct statistical test is vitally important at the statistical analysis stage, and this is described elsewhere
(McCrum-Gardner, 2008). This article has described
the process of sample size calculations including
relevant definitions and examples for different study
designs are provided for illustration. A range of
software packages and websites have also been discussed and evaluated. This information should be
useful for researchers in order to perform sample
size calculations for their research projects. IJTR
Conflict of interest: none
Central Office for Research Ethics Committees (2007)
Question specific guidance on NHS REC application
form (2007). Available from: http://tinyurl.com/yjogwn9
(accessed 17 December 2009)
Cunningham JB, McCrum-Gardner E (2007) Power, effect
and sample size using GPower: practical issues for
researchers and members of research ethics committees.
Evidence Based Midwifery 5(4): 1326
Devane D, Begley C, Clarke M (2004) How many do I need?
Basic principles of sample size estimation. Journal of
Advanced Nursing 47(3): 297302
Florey CV (1993) Sample size for beginners. BMJ 306: 11814
Lenth RV (2001) Some Practical Guidelines for Effective
Sample Size Determination. The American Statistician 55:
18793. Available from http://www.stat.uiowa.edu/techrep/
tr303.pdf (accessed 17 December 2009)
McCrum-Gardner E (2008) Which is the correct statistical
test to use? Brit J Oral Maxillofacial Surgery 46: 3841
Ruperto N (2007) Is Minimal Clinically Important Difference
Relevant for the Interpretation of Clinical Trials in Pediatric
Rheumatic Diseases? The Journal of Rheumatology. http://
tinyurl.com/yjst6ct (accessed 17 December 2009)

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