Lecture 1: Introduction to Statistics
By:
Zinabu Dawit (MPH Epidemiology and
Biostatics)
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At the end of this topic, the students will be able to:

Define statistics/ biostatics

Describe types of statistics

Describe characteristics and limitation of statistics

Discuss application of biostatistics in health sciences
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
Definition of statistics/ biostatistics

Types of Statistics

Characteristics of statistical data

Limitations of statistics

Uses /application of biostatistics
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1.
What is statistics/biostatistics?
2.
What are the roles/uses of biostatistics in health
sciences?
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Definition
Statistics is the field of study that deals with the
collection, organization, summarization, analysis,
and interpretation of masses of numerical data for
understanding a phenomenon or making wise
decisions (inferences) by examining only a small
part of the data.
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Therefore it means two things:
1.Statistical data

Refers to numerical descriptions of things/events
2.Statistical methods

Refers to procedures for collecting,
organizing/presenting, and analyzing masses of
numerical data
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
The statistical methods are employed in many fields

the application of statistical methods to the fields of
life and health sciences( biology, medicine,public
health) is called Biostatistics
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1.Descriptive statistics
Deals with the description of data in a clear and informative
manner using number ,tables and graphs/charts.
Involves the organization and summarization of a body of
data with one or more meaningful tools.
 Helps to identify /describe the general features
/characteristics and trends in a set of data and extracting
useful information
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2. Inferential( Inductive )statistics
Deals with techniques of making conclusions/inference
about the population based on the information obtained
from a sample drawn from that population
Or sample statistics observed are inferred to the
corresponding population parameters
 Inferential statistics builds upon descriptive statistics
Example: Estimation, Hypothesis testing
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1.They must be in aggregates of facts

statistics are number of facts

A single fact, even though numerically stated, cannot be called statistics
2.They must be affected by a multiplicity of causes

Malaria is attributable to factors like Human factors, parasite
factors, and environmental factors
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3.They must be enumerated or estimated according to a
reasonable standard of accuracy
4.They must have been collected in a systematic manner for a
predetermined purpose.
5.They must be placed in relation to each other(must be
comparable.)
 Numerical facts may be placed in relation to each other either in
point of time, space or condition.
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Provide a way of organizing complex data in a suitable form
Assessment of health status
Resource allocation
Health program evaluation
Assessing risk factors
Drawing of inferences(conclusion)
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1.It deals with only those subjects of inquiry that are capable of
being quantitatively measured and numerically expressed.
2.It deals with only on aggregates of facts and no importance is
attached to individual observations
3.Statistical data are only approximately and not
mathematically correct(certain errors involved)
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Variable
 Is
a characteristic which takes different values in
different persons, places, or things.

Any aspect of an individual or object that is measured (e.g.
weight) or recorded (e.g. age) and takes any value.

There may be one or many variable in a study
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1. Qualitative(Categorical)
 Nominal and ordinal
2. Quantitative(Numerical)
 Discrete and continuous
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1.

Categorical variable
A variable which can not be measured in quantitative
form but can only be sorted by name or categories

Not able to be measured as we measure height or weight

The notion of magnitude is absent or implicit.

Categories must not overlap and must cover all possibilities
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Categorical variable is divided into two:
1.


Nominal variable
the values fall into un-ordered categories or classes
Uses names, labels or symbols to assign each
measurement.
Examples: Blood type (A, B, AB, O), sex (male/female)
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2. Ordinal variable
 Assigns
each measurement to one of a limited number of
categories that are ranked in terms of order.

Although non-numerical, can be considered to have a natural
ordering
Examples:1. cancer stages: 1, 2, 3, 4
2. pain severity: no pain, slight pain, moderate pain, severe
pain
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2. Quantitative variable
 A variable
that can be measured or counted and expressed
numerically.
Has
the notion of magnitude.
e.g # of student in this class etc.
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Quantitative variable is divided into two:
1.

Discrete variable
It can only have a limited number of discrete values and
hence takes on integer values only

Characterized by gaps or interruptions in the values.

Both the order and magnitude of the values matter.
e.g. number of children in household(0, 1, 2, 3, etc.)
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2. Continuous variable
 It
can have an infinite number of possible values in any given
interval or within some range

Both the magnitude and the order of the values matter

Does not possess the gaps or interruptions
E.g. Weight(50.123...), Height(1.342...)
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Manipulation of variables

Continuous variables can be discredited
E.g. age(1&1/12-1yr) can be rounded to whole numbers

Continuous or discrete variables can be categorized
E.g. age categories-1(1-5),2(6-10),3(11-15)

Categorical variables can be re-categorized
E.g. marital status(single,married,divorsed,widowed) lumping from 4
categories down to 2 (married,single)
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
The type of unit on which a variable is measured is called a
scale.

Assignment of numbers to subjects, objects or events
according to a set of rules is called Scales of Measurement

All measurements are not the same.
e.g Measuring weight and height of
an individual
have
different scale(wt=---kg vs height=---meter)
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1.Nominal scale


the values fall into un-ordered categories or classes
Simply name differences
Examples:
1.Religion(Orthodox,protestant,muslim,catholic)
2.Marital status(single,married,divorsed)
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2. Ordinal scale

Assigns each measurement to one of a limited number of
categories that are ranked in terms of order

Although non-numerical, can be considered to have a natural
ordering

The numbers have limited meaning 4>3>2>1 is all we know
apart from their utility as labels
Example: social class(very poor,poor,rich,very rich)
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3. Interval scale
-
Differences between any two numbers on a scale are of known size.
(magnitude + constant distance between points)
Example: Temp. in oF on 4 consecutive days
Days:
A
B
Temp. oF: 50 55

C
D
60
65
For these data, not only is day A with 50o F cooler than day D with 65o
but is 15o cooler.

It has no true zero point( “0” is arbitrarily chosen and doesn’t reflect
the absence of temp.)
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4. . Ratio scale
 Measurement
begins at a true zero point and the scale has
equal space (magnitude + constant distance between points
+ true zero)
Examples: length, weight, etc.
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Identify the type of data (nominal, ordinal, interval
and ratio) represented by each of the following.
1.
2.
3.
4.
5.
6.
7.
8.
9.
Blood group N
Temperature (Celsius) I
Ethnic group N
Job satisfaction index (1-5) O
Number of heart attacks O
Calendar year O
Serum uric acid (mg/100ml) O
Number of accidents in 3 - year period O
Number of cases of each reportable disease reported
by a health worker o
10. The average weight gain of 6 1-year old dogs (with a
special diet o
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
Variable types and measurement types have implications :
 On
how data should be displayed or summarized
 Determines
the kind of statistical procedures that should be
used
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THANK YOU!!!!
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