Community Dentistry Department HBS Medical and Dental College , Islamabad

1. DESCRIPTIVE
STATISTICS
DR QURAT-UL-AIN
ASSISTANT PROFESSOR /HOD
COMMUNITY DENTISTRY DEPARTMENT
HBS MEDICAL & DENTAL COLLEGE

2. DEFINITION
1- DESCRIPTIVE
STATISTICS
2-INFERENTIAL
STATISTICS
DEFERENCE
BETWEEN
DESCRIPTIVE
STATISTICS &
INFERENTIAL
STATISTICS
DESCRIPTIVE
STATISTICS
1-MEASURES
OF CENTRAL
TENDENCY
2-MEASURES
OF DISPERSION

3. Statistics can be broken down into two
areas:
•Descriptive Statistics
•Inferential Statistics

4. Descriptive Statistics
•Descriptive statistics is useful because it
allows you to take a large amount of data
and summarize it.

5. For example, let’s say you had data on the incomes of one
million people. No one is going to want to read a million
pieces of data, if you summarize it, it becomes useful

6. Inferential Statistics
•Inferential statistics allows you to make
predictions (“inferences”) from that data.
With inferential statistics, you take data
from samples and make generalizations
about a population.

7. Inferential Statistics
•For example, you might stand in a mall and
ask a sample of 100 people if they like
shopping at H&M. You could make a bar
chart of yes or no answers (that would be
descriptive statistics) or you could use your
research (and inferential statistics) to reason
that around 75-80% of the population (all
shopper with in mall) like shopping at H&M.

9. Difference between
DESCRIPTIVE STATISTICS
• Describes the population
• Using the collected data
• Numerical calculations, graphs,
tables etc.
INFERENTIAL STATISTICS
• Makes inferences about
populations
• Sample data reduced to
meaningful values
• Generalized on the population
• Probability distribution,
hypothesis testing, correlation
testing etc

10. DESCRIPTIVE STATISTICS
•MEASURES OF CENTRALTENDENCY
•MEASURES OF DISPERSION

11. MEASURES OF CENTRALTENDENCY

12. MEASURES OF CENTRALTENDENCY
These are ways of describing the central
position of a frequency distribution for a group
of data.
One number that best sums up the whole set of
measurements, a number that is in some way
"central" to the set.
•Mean
•Median
•Mode

13. MEAN
•Average
•It is the sum of all your observations, divided by
the total number of observations.
•Interpretation tells us around which point data
lies

14. MEAN
EXAMPLE
• Five women in a study on lipid-lowering agents are aged;
52, 55, 56, 58 and 59 years.
• Add these ages together:
• 52 + 55 + 56 + 58 + 59 =280
• Now divide by the number of women: =56
• So the mean age is 56 years.

15. MEDIAN
•The median is the number at which half your
measurements are more than that number and
half are less than that number.
•Middle value
•Example: 12, 14, 16,18, 20, 22, 29

16. MEDIAN
• FORMULA
n+1
2
• n= number of observations
• Data to be organized in ascending or descending order
• If sample number even then two middle values divided
by 2
Example :12, 14, 18, 24, 26, 30,
18+24/ 2= 21

17. MODE
• Most frequently occurring value
• The one that’s found most
Example
12, 15, 16, 22 , 16 , 29, 16, 23, 17, 16
Mode 16
A distribution may have one two three or multiple modes

18. MEASURES OF DISPERSION
• Measures of Spread/ Dispersion: these are ways of summarizing a
group of data by describing how spread out the scores are.

19. For example v looking for investment of
material according to patient flow in a hospital
Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Total #
Patients
Mean =
Hospital
A
5 5 6 6 5 4 31 5.1
Hospital
B
10 5 6 5 2 3 31 5.1

20. • In this case we can not use measure of central
tendency,
• We can use Measure Of Dispersion to interpret this
data
• Measure of dispersions are;
• range
• quartiles,
• absolute deviation
• variance
• Standard deviation

21. Any Question

22. Home task :
measure
mean, median and mode from given data set