The document discusses key concepts in public health methodologies and biostatistics. It defines data as facts that can be processed by computers. Statistics is described as the study of collecting, summarizing, analyzing and interpreting data. Biostatistics applies statistical techniques to health-related fields like medicine. Descriptive statistics refers to methods used to describe data, while inferential statistics are used to draw conclusions from numeric data. Variables, grouped vs. ungrouped data, and types of variables are also outlined.

1. Public Health Methodologies
Biostatistics
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4. Data
• Data is a collection of facts, such as values or
measurements.
OR
• Data is information that has been translated into
a form that is more convenient to move or
process.
OR
• Data are any facts, numbers, or text that can be
processed by a computer.
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5. Statistics
Statistics is the study of the collection,
summarizing, organization, analysis, and
interpretation of data.
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6. Vital statistics
Vital statistics is collecting, summarizing,
organizing, analysis, presentation, and
interpretation of data related to vital events
of life as births, deaths,
marriages, divorces,
health & diseases.
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7. Biostatistics
Biostatistics is the application of statistical
techniques to scientific research in health-
related fields, including medicine, biology,
and public health.
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8. Descriptive Statistics
The term descriptive statistics refers to
statistics that are used to describe. When
using descriptive statistics, every member of a
group or population is measured. A good
example of descriptive statistics is the Census,
in which all members of a population are
counted.
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9. Inferential or Analytical Statistics
Inferential statistics are used to draw
conclusions and make predictions based on the
analysis of numeric data.
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10. Types of Data
• Raw or Primary data: when data collected
having lot of unnecessary, irrelevant & un
wanted information
• Treated or Secondary data: when we treat &
remove this unnecessary, irrelevant & un
wanted information
• Cooked data: when data collected not
genuinely and is false and fictitious
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11. Types of Data – cont.
• Ungrouped data: when data presented or observed individually. For
example if we observed no. of children in 6 families
2, 4, 6, 4, 6, 4
• Grouped data: when we grouped the identical data by frequency.
For example above data of children in 6 families can be grouped as:
No. of children Families
2 1
4 3
6 2
or alternatively we can make classes:
No. of children Frequency
2-4 4
5-7 2
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12. Variable
A variable is something that can be
changed, such as a characteristic or value. For
example age, height, weight, blood pressure
etc
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13. Types of Variable
Independent variable: is typically the
variable representing the value being
manipulated or changed. For example
smoking
Dependent variable: is the observed result of
the independent variable being manipulated.
For example ca of lung
Confounding variable: is associated with both
exposure and disease. For example age is
factor for many events
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