Basics for beginners in statistics Statistics is a branch of science that deals with the study of collection, compilation, analysis, interpretation and presentation of data.

1. BASICS
FOR BEGINNERS IN
BIOSTATISTICS
Dr Lipilekha Patnaik
Professor, Community Medicine
Institute of Medical Sciences & SUM Hospital
Siksha ‘O’Anusandhan deemed to be University
Bhubaneswar, Odisha, India
Email: drlipilekha@yahoo.co.in

2. Session Objectives
• Data, information and statistics
• Variable – concept, types
• Qualitative and quantitative data
• Scales of measurement
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3. DATA
v Data:- “Factsor figures from which conclusions can be
drawn".
v Data can relate to an enormous variety of aspects, for example:
v Weight and height measurementsof students in a class
v Blood pressure and pulse recording of patients at regular
interval
v The temperature of a city (measured every hour) for a
one-week period.
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5. INFORMATION
v “Data when processed(recorded, classified, organized and
interpreted) to give a meaning”.
v Information, can take various forms:
v The no. of studentswith high BMI;
v The no. of personshaving high BP, no. of personswith irregularpulses
v The numberof days during theweek when the temperature went above
30°C.
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6. STATISTICS
• Statistics is a branch of science that deals with the study of
collection, compilation, analysis, interpretation and
presentation of data.
• Biostatistics is branch of science that deals with study of
data derived from biological sciences.
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7. • Descriptive statistics – Gives numerical and graphic
procedures to summarize a collection of data in a clear and
understandable way.
• Inferential statistics – provides procedures to draw
inferences about a population from a sample.
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Types of STATISTICS

8. Patient Sex (M/F) Height (m)
1 M 1.7
2 F 1.6
3 M 1.9
4 F 1.7
5 M 1.6
6 F 1.5
Mean M =1.73 F=1.6
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• To summarize main features
of data from the study sample
(e.g. average height for men
and women in your study)
• Describe important information
from data.
Descriptive Statistics

9. Inferential statistics
•To develop conclusions about a
population based on the people
you studied.
(e.g. are men taller than women?)
•Use sample data to study
associations, or to compare
differences or predictions about a
larger set of data.
Patient Sex (M/F) Height (m)
1 M 1.7
2 F 1.6
3 M 1.9
4 F 1.7
5 M 1.6
6 F 1.5
Mean M =1.73 F=1.6
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10. Types of data
Categorical orqualitative–
• It represents a particularqualityor attribute.
• Few examples
• Religion,
• Gender,
• Colorof hair,
• Blood group (AB O AB ),
• Cured or not cured,
• Grade in examination (AB C D ) (PASS / FAIL).
• These are expressed as numberswithout unit or measurement i.e. numberof persons
with that quality.
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11. Types of data
Quantitative– Numerical data
• Few examples
• Height in cm,
• Weight in kg,
• Hemoglobin(gm%),
• Blood pressure (mm of Hg) &
• Serum bilirubin( mg/dl).
• May be discrete and continuous.
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12. Quantitative Data
Discrete data-
• Values are distinct and separate.
• Values are invariably whole
numbers.
• Eg.
• Age in completed years,
• Number of children in a
family,
• No. of OPV vials opened
during immunization session
Continuousdata –
• Those which have uninterrupted
range of values.
• Possibilityof getting fractionslike
1.2, 3.81 or 75.32.
• It takes all possiblevalues in a certain
range.
• Eg.
• Height (160, 160.2,162.8cmetc)
• Weight (48, 48.2, 48.56 kg)&
• Hemoglobinlevel.
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13. Primary or Secondary data
• Primary data: These are the data obtained directly from an
individual. It gives precise information.
e.g. Height, weight, disease of an individual interviewed is
primary data.
• Secondary data: These are the data obtained from secondary
sources
e.g. Records of a hospital, census data.
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14. Variables
Definition:
• Characteristic (descriptor) of a subject / participant.
• Value varies from subject to subject.
• Examples – Height, Weight, Blood pressure, Disease status
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15. Variables and Hypothesis
• Variables are the cornerstone of research.
• In research we test hypothesis by analyzing the relationship between
variables.
• Independentvariable ® Dependent variable
• Exposure variable ® Outcome variable
• Example-
• Physical activity daily will reduce body weight.
• Smoking causes lung cancer.
• Hypothesis state relationships between variables.
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16. Continuous
Quantitative variables Qualitative variables
Discrete Nominal Ordinal
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17. Why continuous variables should not be made categorical
1. There is a loss of accuracy; precise data (e.g. 26) get smeared across
a class interval (e.g. 20-29).
2. Membership in the same or different class intervals may not be
logical (e.g. 19 in 10-19 vs 20 in 20-29 vs 29 in 20-29).
3. The boundaries of the class intervals are usually arbitrary (why 10,
20, 30 and not 17, 24, 31?).
4. The statistical tests are less powerful.
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18. When continuous variables can be made categorical
1. When the categories are necessary for administrative or
practical purposes
ØE.g. Paediatric VsAdult Vs Geriatric
ØE.g. Mild Vs Moderate Vs Severe hypertension.
2. When the raw data are approximations
ØE.g. for age, as in a rural sample.
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19. Scales of measurements
Nominal scale – When measured by this scale, one simply names or
categorizes responses. They do not imply any ordering among the
responses.
• The information fits into one of the categories.
Eg –gender, religion, blood group, smoking status
Ordinal or rank scale- Characteristic can be put in ordered “natural
categories.”
• Eg –Disease staging systems ( advanced, moderate, mild, none) and
degree of pain (severe, moderate, mild, none), Socio-economic status
scale.
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20. Interval scale – The differences between values can be quantified in
absolute terms and is meaningful.
• Ex- Dates are measured by interval scale since the differences can be
measured in years, Temperature- the difference 80 deg C and 70 deg C is
same as difference between 70 deg C and 60 deg C.
Ratio scale – Permits the comparison of differences of values.
• Ex- Height, weight. Ratio of wt. of 4g is twice that of 2 g. but a temp. of 80
deg C is not twice as hot as 40 deg C.
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Scales of measurements

21. Types of Data
Qualitative Data Quantitative Data
Nominal Ordinal
Discrete Continuous
Interval Ratio
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22. Which type of Data is?
1. Age
2. Systolic blood pressure (mm Hg )
3. Asthma (yes vs. no)
4. Smoking status (never, past, current)
5. Cigarettes smoked, average packs per day (e.g., ½, 1/3, 3/4, 1,
1 1/2, 2)
6. Cigarettes smoked, average packs per day (choose: 0, 1-2, >2)
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• Continuous
or
• Categorical

23. Take – home messages:
§ Type of data – Continuous and Categorical
§ Scales of measurement
Continuous – Ratio and Interval scale
Categorical – Nominal and Ordinal scale
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24. Always Remember
THERE IS STRENGTH IN
NUMBERS

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