Deciphering the dilemma of parametric and nonparametric tests

© 2016 Journal of the Practice of Cardiovascular Sciences | Published by Wolters Kluwer - Medknow 95
Review Article
Introduction
Statistics is a vital tool to provide the inference in medical
research. Choosing an appropriate statistical test plays a vital
role in analysis and interpretation of the research data. In the
past four decades, it has been observed that use of diversified
statistical methods has amplified to a greater extent in medical
research publications; however, it is pertinent to mention
here that the standards of reporting statistical tests and using
them are very low as many shortcomings and pitfalls have
been observed in the studies published in past in various
biomedical journals. This is a serious problem, and it leads to
misleading conclusions, wrong inferences, hazardous clinical
consequences, and utter waste of resources.
With the wider availability of statistical software, performing
statistical analysis has become very easy; however, selection of
an appropriate statistical test is still lacking behind which leads
to wrong study findings and misleading inference. Selection
of an appropriate statistical test depends on (1) nature of the
data, (2) does the data follow normal distribution or not,
and (3) what is the study hypothesis. The potential source of
perplexity while deciding on which statistical test to use for
analyzing the data is whether the data allow for the use of
parametric or nonparametric test procedures. The magnitude
of this concern cannot be underrated. Before selecting the
one between these two, a researcher must be aware about the
underlying differences, advantages, and disadvantages of using
one over the other.[1,2]
Parametric Tests
A parametric test is one which makes assumptions about the
parameters of the population distribution(s) from which the
sample has been drawn. In the parametric test, assumption
is made through sample population. If the information about
the population from which the sample has been drawn is
completely known through its parameters than the test is called
the parametric tests. The common assumptions underlying
parametric tests are as follows:
• The observations must be independent ‑ independence
of observation means that the data are not connected to
any factor that could affect the outcome. For example, a
Deciphering the Dilemma of Parametric and
Nonparametric Tests
Rakesh Kumar Rana, Richa Singhal, Pamila Dua1
Central Council for Research in Ayurvedic Sciences, Ministry of AYUSH, 1
Department of Pharmacology, AIIMS, New Delhi, India
The potential source of complexity while analyzing the data is to choose on whether the data collected could be analyzed properly by the
application of parametric tests or nonparametric tests. This concern cannot be underrated as there are certain assumptions which should
be fulfilled before analyzing the data by applying either of the two types of tests. This article describes in detail the difference between
parametric and nonparametric tests, when to apply which and the advantages of using one over the other.
Key words: Homogeneity of variance, independence, level of measurements, nonparametric, normal distribution, parametric
Access this article online
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Website:
www.j‑pcs.org
DOI:
10.4103/2395-5414.191521
Address for correspondence: Dr. Richa Singhal,
Central Council for Research in Ayurvedic Sciences,
Ministry of AYUSH, 61‑65, Institutional Area, Opp. Janakpuri “D” Block,
New Delhi ‑ 110 058, India.
E‑Mail: richa.singhal2k@gmail.com
How to cite this article: Rana RK, Singhal R, Dua P. Deciphering the
dilemma of parametric and nonparametric tests. J Pract Cardiovasc Sci
2016;2:95-8.
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Rana, et al.: Parametric/Non Parametric Tests
Journal of the Practice of Cardiovascular Sciences ¦ May-August 2016 ¦ Volume 2 ¦ Issue 296
person is hypertensive or not it does not depend on his/her
choice of color. When we talk about the independence of
observations between groups, it means that the patients in
both the groups under study are separate. We do not want
any patient to appear in both the groups
• The observation must be drawn from a normally distributed
population
• The data must be measured on an interval or ratio scale.
Nonparametric Tests
Nonparametric tests are usually referred as distribution‑free
tests. A nonparametric statistical test is the one that does
not necessitate any conditions to be fulfilled about the
parameters of the population from which the sample was
drawn. Nonparametric tests can also be used when the data
are nominal or ordinal.[3,4]
Nonparametric tests are also applied
to the interval or ratio data which do not follow the normal
distribution.
Dilemma of Using Parametric versus
Nonparametric Tests
To simplify the issue, one should remember:
• Scale of measurement of the data ‑ Figure 1 illustrates the
use of parametric or nonparametric tests according to the
measurement scales
• Population distribution ‑ Figure 2 describes the use of
parametric and nonparametric test according to the type
of population distribution
• Homogeneity of Variances ‑ for applying parametric tests,
it must be ensured that the variances of the population are
equal. On the other hand, no such assumption is required
to be fulfilled for application of nonparametric tests
• Independence of samples ‑ for parametric tests to be used the
samples drawn from the population must be independent.
No such assumption is required for nonparametric tests.
Limitations of Nonparametric Tests
Although nonparametric tests do not require any stringent
assumptions to be fulfilled for application, yet parametric
tests are preferred over them due to the following limitations
of nonparametric tests:
• Parametric tests have more statistical power than
nonparametric tests; therefore, they are more likely to
detect a significant difference when it really exists
• Parametric tests can perform well with skewed and
nonnormal data if the sample size is appropriate for
performing the particular parametric tests. For example, for
performing one sample t‑test on a nonnormal data sample
size should be 20, for a two sample t‑test on nonnormal
Types of Tests
Parametric Non Parametric
Interval Scale Ratio Scale
An equal and
definite interval
between two
measurements. It
can be continuous
or discrete. e.g.
Temperature in
centigrade as
50, 51, 52, 53, 54, etc.
Interval between 50
51 is same as
between 53 54
A ratio variable
has all the
properties of an
interval variable
and also has a
clear definition of
zero. Variable
like height in cm,
weight in kg, etc
Nominal Data Ordinal Data
e.g., Gender, Marital Status
Rank or order the
observation as
scores or categories
from or categories from
low to high in terms of
more or less.
Pain scales, opinion
scales
Figure 1: Use of parametric or nonparametric tests according to the scale of measurement of the data.
Population Distribution
Normally distributed Not normally distributed
population or no assumption
can be made about the
population distribution
Parametric tests can
be used
Non parametric tests
have to be used
Figure 2: Use of parametric versus nonparametric tests according to
population distribution.

Journal of the Practice of Cardiovascular Sciences ¦ May-August 2016 ¦ Volume 2 ¦ Issue 2 97
data each group should have more than 15 observations,
and for performing a one‑way analysis of variance on a
nonnormal data having 2–9 groups, each group should
have 15 observations
• Parametric tests can perform better when the (dispersion)
of the groups is different.Although nonparametric tests do
not follow stringent assumptions, yet one assumption that
the dispersion of all the groups must be same is difficult to
be met for running nonparametric tests. If this assumption
of equal dispersion is not met, nonparametric tests may
result in invalid results. Parametric tests can perform better
in such situations
• Inference drawn from parametric tests is easy to interpret
and more meaningful than that of nonparametric tests.
Many nonparametric tests use rankings of the values in
the data rather than using the actual data. Knowing that
the difference in mean ranks between two groups is five
does not really help our intuitive understanding of the data.
On the other hand, knowing that the mean systolic blood
pressure of patients taking the new drug was 5 mmHg lower
than the mean systolic blood pressure of patients on the
standard treatment is both intuitive and useful.
Advantages of Nonparametric Tests
The most important point while analyzing the data is
to understand the fact that whether your data are better
represented by mean or median. This is the key to decide
whether to use a parametric or a nonparametric test. If the data
are better represented by median then use a nonparametric test.
For better understanding of the fact, let us explore an example.
Suppose a researcher is interested in knowing the average
income of the people in two groups and want to compare them.
For this type of data, median will be the appropriate measure
of central tendency, where 50% of the people will be having
income below that and 50% will be having income above that.
If we add some highly paid people in the group than those will
act as outliers and mean will differ to a greater extent, however
income of a particular person will be the same. In that case,
the mean values of the two samples may differ significantly
but medians will not. In such cases, using nonparametric tests
is better than parametric tests.
Table 1: Corresponding table for parametric tests and their nonparametric equivalents
Type of test Level of
measurement
Sample characteristics Correlation
One sample Two sample K samples (i.e., 2)
Independent Dependent Independent Dependent
Parametric Interval or ratio Z‑test or t‑test Independent
sample t‑test
Paired sample
t‑test
One‑way ANOVA Repeated
measure ANOVA
Pearson’s test
Nonparametric Categorical or
nominal
Chi‑square test Chi‑square test Mc‑Nemar test Chi‑square test Cochran’s Q
Rank or ordinal Chi‑square test Mann-Whitney
U‑test
Wilcoxon
signed rank test
Kruskal-Wallis Friedman’s
ANOVA
Spearman’s rho
ANOVA: Analysis of variance
• When the sample size is small and the researcher is not
sure about the normality of the data, it is better to use
nonparametric tests. Because when the sample size is too
small it is not possible to ascertain the normality of the
data because the distribution tests will also lack sufficient
power to provide meaningful results
• When we have ordinal data, nominal data, or some outliers
in the data that cannot be removed then nonparametric tests
must be used.
Parametric Tests and Their Nonparametric
Equivalents
For all the parametric tests, there exists a parallel nonparametric
equivalent. Table 1 describes in brief the type of situation under
study with some examples and the relevant parametric tests and
their nonparametric equivalents to be used in those situations.
How to Use the Online Calculators
for Performing Mann–Whitney U‑Test:
A Nonparametric Test
Suppose a researcher designed a protocol to study the
effectiveness of an analgesic in the patients with arthritis. He/she
recruits 12 participants and randomized them into two groups to
receiveeitherthenewdrugoraplacebo.Participantsareaskedto
record the intensity of pain on a scale of 0–10 where 0 = no pain
and 10 – severe pain.The hypothetical data are shown in Table 2.
The investigator wants to explore the difference in the intensity
of pain in the participants receiving the new drug as compared
to the placebo?
In this example, since the data are on an ordinal scale, hence
we will use Mann–Whitney U‑test to compare both the groups.
The link to the online calculator for performing
Mann–Whitney U‑test is http://scistatcalc.blogspot.in/2013/10/
mann‑whitney‑u‑test‑calculator.html.
Step by step procedure of using this online calculator is
described below:
• Step 1: For the example, explained above for comparing the

Journal of the Practice of Cardiovascular Sciences ¦ May-August 2016 ¦ Volume 2 ¦ Issue 298
number of episodes of shortness of breath in participants
treated with new drug and placebo, we have to enter the
data as comma separated values for both the groups as
shown in Figure 3
• Step 2: Click on perform Mann–Whitney test option and
you are done. The output of the test is shown in Figure 4.
The result obtained from online calculator states that there is a
significant difference between the intensity of pain in the two
groups and P = 0.0104.
Discussion
Parametric and nonparametric are two broad classifications
of the statistical tests. Parametric tests are based on the
assumptions about the population from which the sample
has been drawn; the most common among them is the
assumption of normality. If the underlying assumptions of
the parametric tests are not fulfilled using them may lead to
incorrect conclusions. Utmost care should be taken while
Table 2: Number of episodes of shortness of breath in
the study and placebo group
Intensity of pain on a rating of 0-10
New drug 3 4 2 6 2 5
Placebo 9 7 5 10 6 8
choosing between parametric and nonparametric tests and all
the assumptions related to both of them must be considered
while choosing one over the other.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References
1. Rosner B. Fundamentals of Biostatistics. California: Duxbury Press;
2000.
2. Motulsky H. Intuitive Biostatistics. New York: Oxford University Press;
1995.
3. Walsh JE. Handbook of Nonparametric Statistics. New York: D.V.
Nostrand; 1962.
4. Conover WJ. Practical Nonparametric Statistics. New York: Wiley and
Sons; 1980.
Figure 3: Data setup for a Mann–Whitney test in the online calculator.
Figure 4: Output of the Mann–Whitney test from the online calculator.

Deciphering the dilemma of parametric and nonparametric tests

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