Biostatistics

1. MEASUREMENTS OF HEALTH AND DISEASE
Emiru Merdassa(MSc, Assistant Professor)
Institute of Health Sciences
Department of Public Health
Biostatistics and Epidemiology Unit
12/27/2022
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2. Learning Objectives
By the end of this lesson, the student will be able to
 Define statistics and biostatistics
 Explain variables and scales of measurements
 Compare and contrast a population and a sample
 Identify the role and importance of biostatistical analysis in
public health, medical, and biological research
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3. Definition
 Statistics is a field of study concerned with collection,
organization, summarization and analysis of data and drawing
of inferences about a body of data when only a part of the
data is observed.
 Statistics is the art and science of making decisions in the
face of uncertainty.
 Statistics is the science of learning from data, and of
measuring, controlling, and communicating uncertainty
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4. …Definitions
 A collection of methods for planning experiments,
obtaining, organizing, summarizing, analyzing, interpreting,
presenting and drawing conclusions based on data.
 The word statistics comes from status (meaning “state”).
Early uses involved compilations of data (numbers)
describing various aspects of a state or country.
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5. …Definitions
 Statistics is the science of dealing with numbers.
 Statistics provides a way of organizing data to get
information on a wider and more formal (objective)
basis than relying on personal experience (subjective).
 Everyday decisions are based on incomplete information
 Because of uncertainty, the statements should be
modified
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6. Types of Statistics
 The field of statistics is composed of two broad categories
1. Descriptive Statistics and
2. Inferential Statistics.
 Both of them give us different insights about the data.
 One alone doesn’t not help us much to understand the
complete picture of our data but using both of them
together gives us a powerful tool for description and
prediction.
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7. Population and Sample
Population
 It is the group that is targeted to collect the data from.
 It is always defined first, before starting the data collection
process for any statistical study.
 It is not necessarily be people rather it could be micro-
organism, measurements of rainfall in an area or a group of
people.
 It is the collection of all items of interest or under
investigation
 N represents the population size
 A specific characteristic is called parameter
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8. Population and Sample
Sample
 It is the part of population which is selected randomly for
the study.
 The sample should be selected such that it represents all
the characteristics of the population.
 n represents the sample size
 A specific characteristic is called statistics
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9. Population vs. Sample
a b c d
ef gh i jk l m n
o p q rs t u v w
x y z
Population Sample
b c
g i n
o r u y
Values calculated using population
data are called parameters
Values computed from sample
data are called statistics
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10. …Types of Statistics
1. Descriptive statistics:
 Ways of organizing and summarizing data
 Helps to identify the general features and trends in a set of data
and extracting useful information
 Also very important in conveying the final results of a study
Example: tables, graphs, numerical summary measures
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11. …Descriptive Statistics
 Collect data
e.g., Survey
 Present data
e.g.,Tables and graphs
 Summarize data
e.g., Sample mean = i
X
n

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12. Types of Statistics…
2. Inferential statistics:
 Methods used for drawing conclusions about a population
based on the information obtained from a sample of
observations drawn from that population
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13. …Inferential statistics
 Estimation
 e.g., Estimate the population mean weight using the sample
mean weight
 Hypothesis testing
 e.g.,Test the claim that the population mean weight is 54 Kgs
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14. Basic concepts…
 The tools of statistics are employed in many fields: business,
education, psychology, agriculture, economics, … etc.
 Statistics are everywhere – just look at any newspaper or
the current medical and public health literature.
 When the data analyzed are derived from the biological
science and medicine, we use the term biostatistics to
distinguish this particular application of statistical tools and
concepts.
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15. Biostatistics
• Biostatistics is more than just a compilation of computational
techniques.
• It is not merely pushing numbers through formulas or
computers, but rather it is a way to detect patterns and judge
responses.
• The statistician is both a data detective and judge
• Has central role in medical investigations
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16. Goals of Statistics/biostatistics
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17. ◊ Everything in public health/medicine be it research, diagnosis
or treatment, depends on counting or measurement.
Example: High or low blood pressure has no meaning, unless
it is expressed in figures.
◊ Comparison of a variable in two or more groups. Example:
IMR in developing/developed countries. Hence, biostatistics
may also be called a science of variation.
◊ It gives a dimension to the problem and even suggest the
solution.
…Biostatistics
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18. Uses of biostatistics
 1- Planning, monitoring and evaluating community health care
programs.
 2- Epidemiological research studies.
 3- Diagnosis of community health problems.
 4- Comparison of health status and diseases in different
countries and in one country over years.
 5- To form standards for the different biological measurements
as weight, height.
 6- To differentiate between diseased and normal groups.
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19. Data
 The raw material of statistics is data.
 We may define data as figures. Figures result from the
process of counting or from taking a measurement.
For example:
 When a hospital administrator counts the number of
patients (counting).
 When a nurse weighs a patient (measurement)
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20. Sources of Data
 Routinely kept records. E.g. Hospital medical records
 External sources: Published reports, commercially available
data banks, or the research literature
 Survey: Carried out by trained teams in the field to
investigate health problems
 Experiment: Performed in laboratory or wards
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21. Variable
 It is a characteristic that takes on different values in
different persons, places, or things.
For example:
• heart rate,
• the heights of adult males,
• the weights of preschool children,
• the ages of patients seen in a dental clinic.
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22. Types of variables
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23. QuantitativeVariables
 It can be measured in the
usual sense.
For example:
 the heights of adult males,
 the weights of preschool
children,
 the ages of patients seen in a
dental clinic.
QualitativeVariables
o Many characteristics are not
capable of being measured.
Some of them can be ordered
or ranked.
For example:
o classification of people into
socio-economic groups,
o social classes based on income,
education, etc.
Types of variables
Quantitative Qualitative
…Continued
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24. A discrete variable
 is characterized by gaps or
interruptions in the values that it
can assume.
For example:
 The number of daily admissions to
a general hospital, The number of
decayed, missing or filled teeth
per child in an elementary
school.
A continuous variable
 can assume any value within a specified
relevant interval of values assumed by the
variable.
For example:
 Height, weight, skull circumference.
➢ No matter how close together the observed
heights of two people, we can find another
person whose height falls somewhere in
between.
Types of quantitative variables
Discrete Continuous
Quantitative variables
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25. Scales of measurement
 Measurement is the assignment of numbers to events
or objects according to a set of rules
 There are four types of scales of measurement.
1. Nominal
2. Ordinal
3. Interval
4. Ratio
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26. 1. Nominal scale
 The simplest type of data,
 Unordered categories or classes
 Consists of “naming” observations or classifying them into
various mutually exclusive and collectively exhaustive
categories
 Uses names, labels, or symbols to assign each measurement.
Examples: Blood type, sex, race, marital status,Yes/No
questions etc.
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27. 2. Ordinal scale
 Places events in a meaningful order
 Only permits ranking of items from highest to lowest
Example:
 Dehydration may be ranked as No dehydration, Some
dehydration and Severe dehydration
 Acute Respiratory Infection may be classified as no pneumonia,
pneumonia, severe pneumonia and very severe disease.
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28. Cont’d
Examples
O Patient status, cancer stages, social class, etc.
 No conclusion about whether the difference between first and
second grade is same as the difference between second and
third grade.
 We can not say (very severe disease –severe pneumonia =
pneumonia – no pneumonia).
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29. 3. Interval scale
 Data can be placed in meaningful order, and they have meaningful intervals
between them.
 The intervals can also be measured. In Celsius scale, 100° to 90°C = 60° to
50°C.
 Does not have absolute zero (an arbitrary zero point is assigned), so 100°C
in not equal to twice 50°C (100°C ≠ 2 × 50°C).
 0°C does not indicate complete absence of heat, rather it is the freezing
point of water.
 Intelligent Quotient zero does not indicate complete absence of IQ, but
indicates a serious intellectual problem.
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30. 4. Ratio scale
Measurement begins at a true zero point and the scale
has equal space.
- Examples: Height, age, weight, BP, etc.
Note on meaningfulness of “ratio”-
 Someone who weighs 80 kg is two times as heavy as someone
else who weighs 40 kg.This is true even if weight had been
measured in other measurements.
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31. Level of measurements and Measure scales
Ratio data
Interval data
Ordinal data
Nominal data
Differences between
measurements, true
zero exist
Differences between
measurements, but no
true zero exist
Ordered
categories(ranking,
order or scaling)
Categories (no
ordering or
direction)
Examples
Height,Age, weekly food
spending
Temp in degree Fahrenheit,
standardized exam score
Service quality rating,
Student letter grades
Marital status, types of car of
owned
Highest level (strongest
forms of measurement)
Higher level
Lowest level (Weakest
form of measurement)
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32. Thank you !
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