FACULTY OF SCIENCE & MATHEMATICS QUANTITATIVE RESEARCH METHOD SRU 6024 Assignment: Parametric and Non Parametric Data Prepared for: Dr. Che Nidzam binti Che' Ahmad Prepared by: Putri Nadia Binti Zulkifli M20121000113 Submission date: 29 th November 2013
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PUTRI NADIA BINTI ZULKIFLI M20121000113 3 | P a g e
Data are grouped as Nominal, Ordinal, Interval and Ratio (NOIR) and they are
ordered in their increasing accuracy, powerfulness of measurement, preciseness
and wide application of statistical techniques. Further, the nominal and ordinal data
are qualitative (categorical), whereas interval and ratio data are quantitative
(numerical). There are two broad groups of statistical tests, namely, parametric tests
and non-parametric tests.
Interval and ratio data are parametric, and are used with parametric tools in
which distributions are predictable and often Normal. Nominal and ordinal data are
non-parametric, and do not assume any particular distribution. They are used with
non-parametric tools such as the Histogram.
Parametric data follows particular rules and mathematical algorithms. As a
result detailed conclusions may be drawn about the data. Experiments are thus often
designed to use parametric data. There are very different parametric and non-
parametric tests used in analysis, depending on the type of data you chose during
the design.
Parametric tests require measurements equivalent to at least an interval
scale and assume that certain properties of parent population like:
i) observations are from a normally distributed population
ii) the study is based on large sample (>30)
iii) population parameters like mean, variance, etc. are known.
Non-parametric tests do not depend on the shape of the distribution of the
population and hence are known as distribution-free tests. In other words, they do
not depend on any assumptions about properties or parameters of the parent
population. Most non-parametric tests assume nominal or ordinal data. Non-
parametric tests require more observations than parametric tests to achieve the
same size of Type I and Type II errors. Non-parametric tests have the relative
advantages that they do not require to satisfy stringent assumptions like that of
parametric tests. In other words, non-parametric tests make minimal demands in
terms of pre-requisites. They are also much less cumbersome to use as far ascomputational techniques are concerned. They are most useful when dealing with