It is branch of statistics which deals with
quantitative information. It’s also known summary statistics
statistics aim actually to summarize or explore data of sample
instead of using these data to make predictions or inferences about population
of data (larger group from which sample is selected).
statistics summarizes quantitative data either by numerical measures
such as mean, mode, variance or visually organized forms such as bar charts,
tables, graph, histogram, frequency distribution tables, stem and leaf plot
statistics are distinguished from inferential statistics on
following basis. With descriptive statistics there are simple descriptions
about what data is trying to show done by analysis and summarizing these
collection of information. However in inferential statistics data is actually
used in different way where inferences and predictions are done which is beyond
immediate sets of data presented alone.
statistics simply present quantities data including numerical
facts and measures in much manageable form to be used in research studies
2- Types of descriptive statistics (Main types)
are two major types of descriptive statistics depending on whether simple
summaries provided by descriptive statistics are whether quantitative (these
include various descriptive measures such as mean variance) or visual (these
include simple-understand graphs and tables)
Ø Types of descriptive statistics:
1- Organize Data:
2- Summarize Data:
(or Groups’ “Middle Values”)
Summary of Differences Within Groups)
It actually indicates a
typical value which will be considered as one central number best summarizes
entire set of values or measurements (This calculates
a central value of a data set)
Central tendency measures
values/number of values
It is known as
the ((average)).This actually calculated by adding all values in series of
observation divided by their total number
It’s the most
common measure of central tendency
Mean works best
if distribution of data is very even across range or distributes in
as middle value which divides set of observations into two equal halves
as better measure of central tendency than mean if your
data are skewed
influenced by outliers like mean so best works in case of unbalanced
It’s the value
which occurs with greatest value of frequency in a set of data
It’s useful when
differences are rare or when the differences are non-numerical
It’s not always
a central value and a set of data can have actually more than one mode
or Dispersion refers to another value which actually indicates how far the measurements
are from the mean or from that central value:
difference between greatest and lowest value in a set of data
It is the mean
deviation of all values from the mean.
by getting deviation of each value alone then suaring it to finally add all
squared deviations divided by their (total number minus one)
It considers all
values in series of observation but being in squared units make it unable to be
added or subtracted to mean
Ø Standard deviation:
It is actually
square root of variance
deviation is considered as the “average” degree to which scores
deviate from the mean.
3. Types of variables
In statistics, a
variable has two characteristics:
variable is an attribute that describes a person, place, thing, or idea.
can be qualitative (categorical) or quantitative (numeric).
Ø Qualitative. Qualitative
categories that result in descriptive values or labels. For example the breed
of a dog (e.g., collie, shepherd) the color of a ball (e.g., red, green) would
be examples of qualitative or categorical variables.
Ø Quantitative. Quantitative variables
are numeric. They represent a measurable quantity and can be presented
numerically. For example, what is the difference between having seven apples
and saying that they are delicious, we can count or measure the seven apples
but we can’t put a number for how delicious they are, saying you have seven
apples because they can be presented numerically is quantitative but saying
they are delicious is not quantitative that’s because you can’t measure or
write them in numbers.
of descriptive statics
The importance of
It enables us to
present data in a meaningful way.
Allows for a simpler interpretation of the data.
Therefore using descriptive statistics is useful in summarizing a group of data.