2. Basic Biostatistics 2
In Chapter 1:
1.1 What is Biostatistics?
1.2 Organization of Data
1.3 Types of Measurements
1.4 Data Quality
3. Basic Biostatistics 3
Biostatistics
• Statistics is not merely a
compilation of
computational
techniques
• It is a way of learning
from data
• Biostatistics is
concerned with learning
from biological, public
health, and other health
data
4. Basic Biostatistics 4
Biostatisticians are:
Data detectives who
uncover patterns and clues
through data description and
exploration
Data judges who confirm
and ad adjudicate decision
using inferential methods
5. Basic Biostatistics 5
Measurement
• Measurement ≡ the assigning
of numbers and codes
according to prior-set rules
(Stevens, 1946).
• Three main types of
measurements:
• Categorical (nominal)
• Ordinal
• Quantitative (scale)
6. Basic Biostatistics 6
Categorical
Measurements
Classify observations into named categories
Examples
• HIV status (positive or negative)
• SEX (male or female)
• BLOOD PRESSURE classified as hypo-tensive,
normo-tensive, borderline hypertensive, or
hypertensive
7. Basic Biostatistics 7
Ordinal Measurements
Categories that can be put in rank order
Examples:
• STAGE OF CANCER classified as stage I,
stage II, stage III, stage IV
• OPINION classified as strongly agree
(5), agree (4), neutral (3), disagree (2),
strongly disagree (1); so-called Liekert
scale
9. Basic Biostatistics 9
Example:
Weight Change and Heart Disease
• Investigate effect of weight gain on
coronary heart disease (CHD) risk
• 115,818 women 30- to 55-years of age, all
free of CHD
• Follow over 14 years to determine CHD
occurrence
• Measure the following variables:
Source: Willett et al., 1995
10. Basic Biostatistics 10
Measurement Scales Examples
(cont.)
• Smoker (current, former, no)
• CHD onset (yes or no)
• Family history of CHD (yes or no)
• Non-smoker, light-smoker,
moderate smoker, heavy smoker
• BMI (kgs/m3)
• Age (years)
• Weight presently
• Weight at age 18
Quantitative
vars
Categorical
vars
Ordinal var
11. Basic Biostatistics 11
Variable, Value, Observation
• Observation unit upon which
measurements are made, e.g., person,
place, or thing
• Variable the [generic] thing being
measured, e.g., AGE, HIV status
• Value a realized measurement, e.g., an
age of “27”, a “positive” HIV test
12. Basic Biostatistics 12
Data Collection Form
Data Collection Form
Var1 (ID) 1
Var2 (AGE) 27
Var3 (SEX) F
Var4 (HIV) Y
Var5 (KAPOSISARC) Y
Var6 (REPORTDATE)4/25/89
Var7 (OPPORTUNIS) N
Each questionnaire
contains an
observation
Each question
corresponds to a
variable
14. Basic Biostatistics 14
Data Table
• Each row corresponds to an observation
• Each column contains information on a variable
• Each cell in the table contains a value
AGE SEX HIV ONSET INFECT
24 M Y 12-OCT-07 Y
14 M N 30-MAY-05 Y
32 F N 11-NOV-06 N
15. Basic Biostatistics 15
Data Table Example 2: Cigarette Use
and Lung Cancer
Unit of observation is region, not individual
Variables
cig1930 = per capita
cigarette use in 1930
mortality = lung
cancer mortality per
100,000 in 1950
16. Basic Biostatistics 16
Data Quality
• An analysis is only as good as its data
• GIGO ≡ garbage in, garbage out
• Validity = freedom from systematic error
• Objectivity = seeing things as they are
without making it conform to a worldview
• Consider how the wording of a question
can influence validity and objectivity
17. Basic Biostatistics 17
Choose Your Ethos
BS is manipulative and has a preferred outcome.
Science bends over backwards to consider alternatives.
Blackburn, S. (2005).
Oxford Univ. Press
Frankfurt, H. G. (2005).
Princeton University Press
18. Basic Biostatistics 18
Scientific Ethos
“I cannot give any scientist of any age
any better advice than this:
The intensity of the conviction that a
hypothesis is true has no bearing on
whether it is true or not.”
Peter Medawar
