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International Encyclopedia of Ergonomics and Human Factors, 2006, Second Edition, Volume 3

Edited by Waldemar Karwowski, Boca Raton, FL: CRC Press

3084

Determining Usability Test Sample Size

Carl W. Turner

*

, James R. Lewis

†

, and Jakob Nielsen

‡

*

State Farm Insurance Cos., Bloomington, IL 61791, USA

†

IBM Corp., Boca Raton, FL 33487 USA

‡

Nielsen Norman Group, Fremont, CA 94539, USA

1 INTRODUCTION

Virzi (1992), Nielsen and Landauer (1993), and Lewis

(1994) have published influential articles on the topic of

sample size in usability testing. In these articles, the authors

presented a mathematical model of problem discovery rates

in usability testing. Using the problem discovery rate

model, they showed that it was possible to determine the

sample size needed to uncover a given proportion of

problems in an interface during one test. The authors

presented empirical evidence for the models and made

several important claims:

• Most usability problems are detected with the first

three to five subjects.

• Running additional subjects during the same test is

unlikely to reveal new information.

• Return on investment (ROI) in usability testing is

maximized when testing with small groups using

an iterative test-and-design methodology.

Nielsen and Landauer (1993) extended Virzi’s (1992)

original findings and reported case studies that supported

their claims for needing only small samples for usability

tests. They and Lewis (1994) identified important

assumptions about the use of the formula for estimating

problem discovery rates. The problem discovery rate model

was recently re-examined by Lewis (2001).

2 THE ORIGINAL FORMULAE

Virzi (1992) published empirical data supporting the use of

the cumulative binomial probability formula to estimate

problem discovery rates. He reported three experiments in

which he measured the rate at which usability experts and

trained student assistants identified problems as a function

of the number of naive participants they observed. Problem

discovery rates were computed for each participant by

dividing the number of problems uncovered during an

individual test session by the total number of unique

problems found during testing. The average likelihood of

problem detection was computed by averaging all

participants’ individual problem discovery rates.

Virzi (1992) used Monte Carlo simulations to

permute participant orders 500 times to obtain the average

problem discovery curves for his data. Across three sets of

data, the average likelihoods of problem detection (p in the

formula above) were 0.32, 0.36, and 0.42. He also had the

observers (Experiment 2) and an independent group of

usability experts (Experiment 3) provide ratings of problem

severity for each problem. Based on the outcomes of these

experiments, Virzi made three claims regarding sample size

for usability studies: (1) Observing four or five participants

allows practitioners to discover 80% of a product’s usability

problems, (2) observing additional participants reveals

fewer and fewer new usability problems, and (3) observers

detect the more severe usability problems with the first few

participants. Based on these data, he claimed that running

tests using small samples in an iterative test-and-design

fashion would identify most usability problems and save

both time and money.

Proportion of unique problems

found = 1 – (1 – p)

n

(1)

where p is the mean problem discovery rate computed

across subjects (or across problems) and n is the number of

subjects.

Seeking to quantify the patterns of problem

detection observed in several fairly large-sample studies of

problem discovery (using either heuristic evaluation or user

testing) Nielsen and Landauer (1993) derived the same

formula from a Poisson process model (constant probability

path independent). They found that it provided a good fit to

their problem-discovery data, and provided a basis for

predicting the number of problems existing in an interface

and performing cost-benefit analyses to determine

appropriate sample sizes. Across 11 studies (five user tests

and six heuristic evaluations), they found the average value

of p to be .33 (ranging from .16 to .60, with associated

estimates of p ranging from .12 to .58). Nielsen and

Landauer used lambda rather than p, but the two concepts

are essentially equivalent. In the literature, ? (lambda), L,

and p are commonly used to represent the average

likelihood of problem discovery. Throughout this article,

we will use p.

Number of unique problems

found = N(1 – (1 – p)

n

)) (2)

Determining Usability Test Sample Size 3085

where p is the problem discovery rate, N is the total number

of problems in the interface, and n is the number of subjects.

The problem discovery rate was approximately .3

when averaged across a large number of independent tests,

but the rate for any given usability test will vary depending

on several factors (Nielsen & Landauer, 1993). These

factors include:

• Properties of the system and interface, including

the size of the application.

• Stage in the usability lifecycle the product is tested

in, whether early in the design phase or after

several iterations of test and re-design.

• Type and quality of the methodology used to

conduct the test.

• Specific tasks selected.

• Match between the test and the context of real

world usage.

• Representativeness of the test participant.

• Skill of the evaluator.

Research following these lines of investigation led to other,

related claims. Nielsen (1994) applied the formula in

Equation 2 to a study of problem discovery rate for heuristic

evaluations. Eleven usability specialists evaluated a

complex prototype system for telephone company

employees. The evaluators obtained training on the system

and the goals of the evaluation. They then independently

documented usability problems in the user interface based

on published usability heuristics. The average value of p

across 11 evaluators was .29, similar to the rates found

during talk-aloud user testing (Nielsen & Landauer, 1993;

Virzi, 1992).

Lewis (1994) replicated the techniques applied by

Virzi (1992) to data from a usability study of a suite of

office software products. The problem discovery rate for

this study was .16. The results of this investigation clearly

supported Virzi’s second claim (additional participants

reveal fewer and fewer problems), partially supported the

first (observing four or five participants reveals about 80%

of a product’s usability problems as long as the value of p

for a study is in the approximate range of .30 to .40), and

failed to support the third (there was no correlation between

problem severity and likelihood of discovery). Lewis noted

that it is most reasonable to use small-sample problem

discovery studies “if the expected p is high, if the study will

be iterative, and if undiscovered problems will not have

dangerous or expensive outcomes” (1994, p. 377).

3 RECENT CHALLENGES

Recent challenges to the estimation of problem discovery

rates appear to take two general forms. The first questions

the reliability of problem discovery procedures (user testing,

heuristic evaluation, cognitive walkthrough, etc.). If

problem discovery is completely unreliable, then how can

anyone model it? Furthermore, how can one account for the

apparent success of iterative problem-discovery procedures

in increasing the usability of the products against which they

are applied?

The second questions the validity of modeling the

probability of problem discovery with a single value for p.

Other issues – such as the fact that claiming high

proportions of problem discovery with few participants

requires a fairly high value of p, that different task sets lead

to different opportunities to discover problems, and the

importance of iteration – are addressed at length in earlier

papers (Lewis, 1994; Nielsen, 1993).

3.1 Is Usability Problem Discovery Reliable?

Molich et al. (1998) conducted a study in which four

different usability labs evaluated a calendar system and

prepared reports of the usability problems they discovered.

An independent team of usability professionals compared

the reports produced by the four labs. The number of

unique problems identified by each lab ranged from four to

98. Only one usability problem was reported by all four

labs. The teams that conducted the studies noted difficulties

in conducting the evaluations that included a lack of testing

goals, no access to the product development team, a lack of

user profile information, and no design goals for the

product.

Kessner et al. (2001) have also reported data that

question the reliability of usability testing. They had six

professional usability teams test an early prototype of a

dialog box. The total number of usability problems was

determined to be 36. None of the problems were identified

by every team, and only two were reported by five teams.

Twenty of the problems were reported by at least two teams.

After comparing their results with those of Molich et al.

(1999), Kessner et al. suggested that more specific and

focused requests by a client should lead to more overlap in

problem discovery.

Hertzum and Jacobsen (2001) have termed the lack

of inter-rater reliability among test observers an ‘evaluator

effect’ – that “multiple evaluators evaluating the same

interface with the same usability evaluation method detect

markedly different sets of problems” (p. 421). Across a

review of 11 studies, they found the average agreement

between any two evaluators of the same system ranged from

5% to 65%, with no usability evaluation method (cognitive

walkthroughs, heuristic evaluations, or think-aloud user

studies) consistently more effective than another. Their

review, and the studies of Molich et al. (1999) and Kessner

et al. (2001) point out the importance of setting clear test

objectives, running repeatable test procedures, and adopting

clear definitions of usability problems. Given that multiple

evaluators increase the likelihood of problem detection

(Nielsen, 1994), they suggested that one way to reduce the

evaluator effect is to involve multiple evaluators in usability

tests.

3086 Determining Usability Test Sample Size

The results of these studies are in stark contrast to

earlier studies in which usability problem discovery was

reported to be reliable (Lewis, 1996; Marshall, Brendon, &

Prail, 1990). The widespread use of usability problem

discovery methods indicates that practitioners believe they

are reliable. Despite this widespread belief, an important

area of future research will be to reconcile the studies that

have challenged the reliability of problem discovery with

the apparent reality of usability improvement achieved

through iterative application of usability problem discovery

methods. For example, there might be value in exploring

the application of signal detection theory (Swets, Dawes, &

Monahan, 2000) to the detection of usability problems.

3.2 Issues in the Estimation of p

Woolrych and Cockton (2001) challenged the

assumption that a simple estimate of p is sufficient for the

purpose of estimating the sample size required for the

discovery of a specified percentage of usability problems in

an interface. Specifically, they criticized the formula for

failing to take into account individual differences in

problem discoverability and also claimed that the typical

values used for p (around .30) are overly optimistic. They

also pointed out that the circularity in estimating the key

parameter of p from the study for which you want to

estimate the sample size reduces its utility as a planning

tool. Following close examination of data from a previous

study of heuristic evaluation, they found combinations of

five participants which, if they had been the only five

participants studied, would have dramatically changed the

resulting problems lists, both for frequency and severity.

They recommended the development of a formula that

replaces a single value for p with a probability density

function.

Caulton (2001) claimed that the simple estimate of

p only applies given a strict homogeneity assumption – that

all types of users have the same probability of encountering

all usability problems. To address this, Caulton added to the

standard cumulative binomial probability formula a

parameter for the number of heterogeneous groups. He also

introduced and modeled the concept of problems that

heterogeneous groups share and those that are unique to a

particular subgroup. His primary claims were (1) the more

subgroups, the lower will be the expected value of p and (2)

the more distinct the subgroups are, the lower will be the

expected value of p.

Most of the arguments of Woolrych and Cockton

(2001) were either addressed in previous literature or do not

stand up against the empirical findings reported in previous

literature. It is true that estimates of p can vary widely from

study to study. This characteristic of usability testing can be

addressed by estimating p for a study after running two

subjects and adjusting the estimate as the study proceeds

(Lewis, 2001). There are problems with the estimation of p

from the study to which you want to apply it, but recent

research (discussed below) provides a way to overcome

these problems. Of course, it is possible to select different

subsets of participants who experienced problems in a way

that leads to an overestimate of p (or an underestimate of p,

or any value of p that the person selecting the data wishes).

Test administrators should follow accepted practice and

select evaluators who represent the range of knowledge and

skills found in the population of end users. There is no

compelling evidence that a probability density function

would lead to an advantage over a single value for p,

although there might be value in computing confidence

intervals for single values of p.

Caulton’s (2001) refinement of the model is

consistent with the observation that different user groups

expose different types of usability problems (Nielsen, 1993).

It is good practice to include participants from significant

user groups in each test; three or four per group for two

groups and three participants for more than two groups. If

there is a concern that different user groups will uncover

different sets of usability problems then the data for each

group can be analyzed separately, and a separate p

computed for each user group. However, Caulton’s claim

that problem discovery estimates are always inflated when

averaged across heterogeneous groups and problems with

different values of p is inconsistent with the empirical data

presented in Lewis (1994). Lewis demonstrated that p is

robust, showing that the mean value of p worked very well

for modeling problem discovery in a set of problems that

had widely varying values of p.

4 IMPROVING SMALL-SAMPLE

ESTIMATION OF p

Lewis (2001), responding to an observation by Hertzum and

Jacobsen (2001) that small-sample estimates of p are almost

always inflated, investigated a variety of methods for

adjusting these small-sample estimates to enable accurate

assessment of sample size requirements and true proportions

of discovered problems. Using data from a series of Monte

Carlo studies applied against four published sets of problem

discovery databases, he found that a technique based on

combining information from a normalization procedure and

a discounting method borrowed from statistical language

modeling produced very accurate adjustments for small-

sample estimates of p. The Good-Turing (GT) discounting

procedure reduced, but did not completely eliminate, the

overestimate of problem discovery rates produced by small-

sample p estimates. The GT adjustment, shown in Equation

3, was:

(3)

where p

est

is the initial estimate computed from the raw data

of a usability study, E(N

1

) was the number of usability

Determining Usability Test Sample Size 3087

problems detected by only one user, and N was that total

number of unique usability problems detected by all users.

By contrast, the normalization procedure (Norm)

slightly underestimated problem discovery rates. The

equation was:

(4)

where p

est

is the initial estimate computed from the raw data

of a usability study and n was the number of test

participants. He concluded that the overestimation of p

from small-sample usability studies is a real problem with

potentially troubling consequences for usability

practitioners, but that it is possible to apply these procedures

(normalization and Good-Turing discounting) to

compensate for the overestimation bias. Applying each

procedure to the initial estimate of p, then averaging the

results, produces a highly accurate estimate of the problem

discovery rate. Equation 5 shows the formula for an

adjusted p estimate based on averaging Good-Turing and

normalization adjustments.

(5)

“Practitioners can obtain accurate sample size estimates for

problem-discovery goals ranging from 70% to 95% by

making an initial estimate of the required sample size after

running two participants, then adjusting the estimate after

obtaining data from another two (total of four) participants”

(Lewis, 2001, p.474).

The results of a return-on-investment (ROI) model

for usability studies (Lewis, 1994) indicated that the

magnitude of p affected the point at which the percentage of

problems discovered maximized ROI. For values of p

ranging from .10 to .5, the appropriate problem discovery

goal ranged from .86 to .98, with lower values of p

associated with lower problem discovery goals.

5 AN APPLICATION OF THE

ADJUSTMENT PROCEDURES

In the example shown in Table 1, a usability test with eight

participants has led to the discovery of four unique usability

problems. The problem discovery rates (p) for individual

participants ranged from 0.0 to .75. The problem discovery

rates for specific problems ranged from .125 to .875. The

average problem discovery rate (averaged either across

problems or participants), p

est

, was .375. Note that

Problems 2 and 4 were detected by only one participant

(Participants 2 and 7, respectively). Applying the Good-

Turing estimating procedure from Equation 3 gives

TABLE 1

Data from a Hypothetical Usability Test with Eight

Subjects, p

est

= .375

Problem Number

Subject 1 2 3 4 Count

p

1 1 0 1 0 2 0.500

2 1 0 1 1 3 0.750

3 1 0 0 0 1 0.250

4 0 0 0 0 0 0.000

5 1 0 1 0 2 0.500

6 1 0 0 0 1 0.250

7 1 1 0 0 2 0.500

8 1 0 0 0 1 0.250

Count 7 1 3 1

P 0.875 0.125 0.375 0.125 0.375

Applying normalization as shown in Equation 4 gives

The adjusted problem discovery rate is obtained by

averaging the two estimates as shown in Equation 5 gives

With this adjusted value of p and the known sample size, it

is possible to estimate the sample size adequacy of this

study using the cumulative binomial probability formula: 1

– (1 – .25)

8

= .90. If the problem discovery goal for this

study had been 90%, then the sample size was adequate. If

the discovery goal had been lower, the sample size would be

excessive, and if the discovery goal had been higher, the

sample size would be inadequate. The discovery of only

four problems (one problem for every two participants)

suggests that the discovery of additional problems would be

difficult. If four problems constitute 90% of the problems

available for discovery given the specifics of this usability

study, then 100% of the problems available for discovery

should be about 4/.9, or 4.44. In non-numerical terms, there

probably aren’t a lot of additional problems to extract from

this problem discovery space.

As an example of sample size estimation, suppose

you had data from the first four participants and wanted to

estimate the number of participants you’d need to run to

achieve 90% problem discovery. After running the fourth

participant, there were three discovered problems (because

Problem 2 did not occur until Participant 7), as shown in

Table 2. One of those problems (Problem 4) occurred only

once.

3088 Determining Usability Test Sample Size

TABLE 2

Data from a Hypothetical Usability Test; First Four

Subjects, p

est

= .500

Problem Number

Subject 1 3 4 Count p

1 1 1 0 2 0.667

2 1 1 1 3 1.000

3 1 0 0 1 0.333

4 0 0 0 0 0.000

Count 3 2 1

P 0.750 0.500 0.250 0.500

Applying the Good-Turing estimating procedure from

Equation 3 gives

Applying normalization as shown in Equation 4 gives

The average of the two estimates is

Given p = .28, the estimated proportion of discovered

problems would be 1 – (1 – .28)

4

, or .73. Doing the same

computation with n = 7 gives .90, indicating that the

appropriate sample size for the study would be 7. Note that

in the matrix for this hypothetical study, running the eighth

participant did not reveal any new problems.

6 CONCLUSIONS

The cumulative binomial probability formula (given

appropriate adjustment of p when estimated from small

samples) provides a quick and robust means of estimating

problem discovery rates (p). This estimate can be used to

estimate usability test sample size requirements (for studies

that are underway) and to evaluate usability test sample size

adequacy (for studies that have already been conducted).

Further research is needed to answer remaining questions

about when usability testing is reliable, valid, and useful.

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