INTRODUCTION TO BIOSTATISTICS
By: Samuel Fikadu (M.Sc. in biostatistics)
Email: safekadu09@gmail.com
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Outlines of the course (chapters)
1. Introduction
2. Methods of Data Collection and Presentation
3. Measures of Central Tendency and Variation
4. Elementary Probability
5. Probability Distribution
6. Sampling and Sampling Distributions
7. Estimation and Hypothesis Testing
8. Measures of associations
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1. Introduction
1. Definition
• The term statistics has two definitions;
– When used in a singular sense
– When used in its plural sense
• In its plural sense, it is equivalent to numerical facts,
figures or measurements.
• But all numerical figures are not statistics.
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Statistics in its Singular Sense: (field of study/subject matter)
• The branch of applied research that deals with the development
and application of methods for collecting, organizing,
presenting, analyzing, and interpreting numerical data.
• Biostatistics is the branch of statistics responsible for the
proper interpretation of scientific data generated in biology,
public health, and other health sciences (i.e., the biomedical
sciences).
• In other words statistical processes and methods applied to the
collection, analysis, and interpretation of biological data and
especially data relating to human biology, health, and medicine.
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Definition of Some Basic Terms
• Population is the complete set of possible measurements for
which inferences are to be made.
• Census: a complete enumeration of the population. But in
most real problems it cannot be realized, hence we take
samples.
• Sample: A sample from a population is the set of
measurements that are actually collected in the course of an
investigation.
• A sample survey: is a study that obtains data from a subset of
a population, in order to estimate population attributes
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• Parameter: Characteristic or measure obtained from a
population.
• Statistic: Characteristic or measure obtained from a sample.
• Sampling: The process or method of sample selection from the
population.
• Sample size: The number of elements or observations to be
included in the sample.
• Variable: It is an item of interest that can take on many
different numerical values.
• Data: refers to a collection of facts, values, observations, or
measurements that the variables can assume. 6
Classifications of biostatistics
1. Descriptive Statistics
❖ A statistical method that is concerned with the collection,
organization, summarization, and analysis of data from a sample or
population.
❖ With descriptive statistics we are simply describing what is or what
the data shows (describes existing situation).
2. Inferential Statistics
❖ A statistical method that is concerned with drawing conclusions/
inferring about a particular population by selecting and measuring a
random sample from the population.
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Types of Variables and Measurement Scales
A variable is a characteristic or attribute that can assume
different values in different persons, places, or things.
Examples :
▪ Age
▪ diastolic blood pressure
▪ heart rate
▪ the height of adult males
▪ the weights of preschool children
▪ gender of students
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Types of Variables/Data
A. Based on information contained in the data
1. Qualitative Variables/data
❖ Non-numeric variables, and can't be measured.
Examples: gender, religious affiliation, state of birth,
2. Quantitative Variables/data
❖ numerical variables that can be measured. E.g. number of
patients in the given hospital, etc.
❖ Quantitative Variables can be either discrete or continuous,
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Discrete Variables
• are variables that assume a finite or countable number of possible
values.
• are usually obtained by counting.
• is characterized by gaps or interruptions in the values that it can
assume.
• These gaps or interruptions indicate the absence of values
between particular values that the variable can assume.
Example:
• The number of daily admissions to a general hospital
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Continuous Variables
• are variables that assume an infinite number of possible
values between any two specific values.
• are usually obtained by measurement.
• does not possess the gaps or interruptions characteristic of a
discrete variable.
Example:
• Weight, age, length, temperature, speed, salary, and the mark
of students
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B. On the basis of the measurement scales :
1. The nominal data
Only "naming" and classifying with no rank between the
observations.
When numbers are assigned to categories, it is only for
coding purposes and it does not provide a sense of size.
Example: Sex of a person (M, F), eye color (e.g. brown, blue),
religion (Muslim, Christian), place of residence (urban, rural),
etc.
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2. Ordinal scale
Categorization and ranking (ordering) observations is
possible.
We can talk of greater than or less than and it conveys
meaning to the value but;
Impossible to express the real difference between
measurements in numerical terms.
Examples: Socioeconomic status (very low, low, medium, high,
very high), severity (mild, moderate, severe), blood pressure
(very low, low, high, very high, etc.
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3. Interval Scale
– Possible to categorize, rank, and tell the real distance between
any two measurements.
– There is no fixed zero (meaningful) or Zero is not absolute.
Examples:
❖ Body temperature in OF or OC (measured in degrees). It is
meaningful to say the difference between 30oC & 40 oC and
25oC & 35oC is equal (i.e. 10 oC).
❖ IQ of students in the class.
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4. Ratio scale
– The highest level of measurement scale, characterized
by the fact that equality of ratios as well as equality of
intervals can be determined.
– There is a true zero point. i.e. zero is absolute.
Example:
volume, height, weight, length, number of items, etc.
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C. On the basis of their source :
1. Primary data
Data generated for the first time primarily/originally
for the study in question.
2. Secondary data
Data obtained from other pre-existing/ priorly
collected sources.
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Stage of statistical investigation
1. Collection of data
❖ The process of obtaining measurements or counts.
2. Organization of data
❖Includes editing, classifying, and tabulating the data collected.
3. Presentation of data:
❖overall view of what the data actually looks like.
❖facilitate further statistical analysis.
❖Can be done in the form of tables and graphs or diagrams.
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4. Analysis of data
❖ To dig out useful information for decision making
❖ It involves extracting relevant information from the data
(like mean, median, mode, range, and variance…) using
elementary mathematical operations.
5. Interpretation of data
❖ Concerned with drawing conclusions from the data collected
and analyzed; and giving meaning to analysis results.
❖ A difficult task that requires a high degree of skill and
experience.
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