Descriptive statistics are used to summarize and describe data through measures like means and percentages. They aim to describe a sample rather than make inferences about the underlying population. Parametric statistics assume the data comes from a known probability distribution and allow inferences about the distribution's parameters, but require the data to meet certain assumptions. Non-parametric methods make fewer assumptions and allow comparisons of ordinal data, making them more robust and widely applicable than parametric methods.

1. Reporter: Norhac T. Kali
Research Methods in Public Administration
MPA 452

2. Descriptive
Statistics
Parametric
Statistics
Non-Parametric
Statistics

3. is the discipline of quantitatively describing the
main features of a collection of information, or the
quantitative description itself.
are distinguished from inferential
statistics (or inductive statistics), in that
descriptive statistics aim to summarize a sample,
rather than use the data to learn about
the population that the sample of data is thought
to represent.

4. The shooting percentage in basketball is a descriptive
statistic that summarizes the performance of a player or a
team. This number is the number of shots made divided by
the number of shots taken. For example, a player who
shoots 33% is making approximately one shot in every
three. The percentage summarizes or describes multiple
discrete events. Consider also the grade point average. This
single number describes the general performance of a
student across the range of their course experiences.
in the business world, descriptive statistics provides a useful
summary of many types of data. For example, investors and
brokers may use a historical account of return behavior by
performing empirical and analytical analysis on their
investments in order to make better investing decisions in
the future.

5. is a branch of statistics which assumes that
the data has come from a type of probability
distribution and makes inferences about the
parameters of the distribution.
Researchers must make sure their data meets
a number of assumptions (or parameters)
before these tests can be used properly.

6. Suppose we have a sample of 99 test scores with a mean of
100 and a standard deviation of 1. If we assume all 99 test
scores are random samples from a normal distribution we
predict there is a 1% chance that the 100th test score will be
higher than 102.365 (that is the mean plus 2.365 standard
deviations) assuming that the 100th test score comes from
the same distribution as the others. The normal family of
distributions all have the same shape and
are parameterized by mean and standard deviation. That
means if you know the mean and standard deviation, and
that the distribution is normal, you know the probability of
any future observation. Parametric statistical methods are
used to compute the 2.365 value above, given
99 independence observations from the same normal
distribution.
A non-parametric estimate of the same thing is the
maximum of the first 99 scores. We don't need to assume
anything about the distribution of test scores to reason that
before we gave the test it was equally likely that the highest
score would be any of the first 100. Thus there is a 1%
chance that the 100th is higher than any of the 99 that

7. refers to comparative properties (statistics) of the data, or
population, which do not include the typical parameters, of mean,
variance, standard deviation, etc.
Non-parametric methods are widely used for studying populations
that take on a ranked order (such as movie reviews receiving one
to four stars). The use of non-parametric methods may be
necessary when data have a ranking but no clear numerical
interpretation, such as when assessing preferences. In terms
of levels of measurement, non-parametric methods result in
"ordinal" data.
As non-parametric methods make fewer assumptions, their
applicability is much wider than the corresponding parametric
methods. In particular, they may be applied in situations where
less is known about the application in question. Also, due to the
reliance on fewer assumptions, non-parametric methods are
more robust.