Biostatistics

1. Biostatistics
By
Dr. Nazar Ahmed Al-Jaf

2. Statistics is a field of study that means different thing to different
people. To some it indicates mathematics, or arithmetic. to others it
has to do with figures or numbers that may not be easily
comprehensible. There are still a few who perceive statistics as
abstract subject and are scared on hearing the name mentioned.
However statistics is part of us and has application in our daily
activities. For instance how many people reside in your household
especially in polygamous setting? How many are male and how
many are females? How many are educated beyond secondary
school level? In all the observations therefore you are intentionally
or un intentionally applying principles of statistics to some extent.
Introduction

3.  The word “Statistic”. Derived form Latin word status,
meaning "manner of standing" or "position“. Simply it
means data or numbers.
 Statistics is a branch of knowledge that deals with the
organization and summarization of data and drawing
inferences about large volumes of data after a part of it
is observed.
 In its modern setting, statistics refers to the science of
collection, analysis, and interpretation of data.

4. Biostatistics:
 The tools of statistics are employed in many fields:
business, education, psychology, agriculture, economics,
… etc.
 When the data analyzed are derived from the biological
science, medicine, and health sciences, they are referred
to as Biostatistics
 We use the term biostatistics to distinguish this
particular application of statistical tools and concepts.

5. Data
• The raw material of statistics is data.
• Data may define data as figures (numbers). 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 weights a patient (measurement).

6. Sources of data
1. Routinely kept records: Hospital medical records contain
immense amounts of information on patients.
2. External sources: The data already exist in the form of
published reports, commercially available data banks, or
the research literature, i.e. someone else has already
asked the same question.
3. Surveys: A survey may be conducted to obtain
unanswered information.
4. Experiments: Frequently the data needed to answer a
question are available only as the result of an experiment.

7. Variable
• This is derived from variation in living and non-living
things.
• A variable is any characteristic that can and does
assume different values from person to person in a
population or sample of interest.
• For example, demographic variables describe basic
characteristics of human populations, such as gender,
age, ethnicity, marital status, number of children,
education level, employment status, and income.

8. Quantitative Variables
It can be measured by a
numeric amount.
For example:
- the heights of adult males,
- the weights of preschool
children,
- the ages of patients seen in
a dental clinic.
Qualitative Variables
Many characteristics are not
capable of being measured
by a numeric amount.
Some of them can be
ordered or ranked.
For example:
- classification of people into
socio-economic groups,
- social classes based on
income, education, etc.
Types of variables

9. 1. Discrete Variables/ Categorical variables
These are those variables that assume whole numbers such as
0,1,2,3. but not 2.6 or 3.415etc e.g.:
- the number of patients in a hospital
- the number of students in a class
- the number of children in a family
2. Continuous Variables
These are those variables that can assume values other than whole
numbers e.g. the height of an individual:
- The weight of a motor car
- The age of an individual
- The weight of an individual which can be 127 kg, 128.2kg etc.

10. Types of variables
1. Independent Variable: -
Is presumed cause, manipulated by researcher to
observe the effect.
2. Dependent Variable: -
Is the response or outcome the researcher wish to
explain or predict.

11. Measurement: Is the process of assigning numbers to variables.
The four levels of measurement are:
1. Nominal level of measurement: A nominal scale is the lowest
form of measurement because the numbers are simply used as
labels, representing categories or characteristics, and there is no
order to the categories (i.e., no category is higher or lower).
Examples of nominal variables are gender (male, female), religion
(Muslim, Christian), marital status (married, unmarried), and
region of residence (urban, rural).

12. 2. Ordinal level of measurement: the variables are
ordered according to a scale that shows the
relationship between them and their greatness. For
example:
• Anxiety levels of people in a therapy group might be
categorized as mild, moderate, and severe.
• Knowledge of students in a class might be
categorized as high, moderate, and low.

13. 3. Interval level of measurement: The distance between the ranks. For
example, a reading of 37°C might be one category, 37.2°C might be
another category, and 37.4°C might constitute a third category.
there is 0.2°C difference between the first and second category and
third category.
4. Ratio level of measurement: is the lowest form of measurement.
data that can be categorized and ranked; in addition, the distance
between ranks can be specified, and a “true” or natural zero point
can be identified. For example, the number of pain medication
requests made by patients, it would be possible for some patients
to request no pain medications.

15. Population
 It is the largest collection of values of a random
variable for which we have an interest at a particular
time.
 For example: The weights of all the children enrolled
in a certain elementary school.
 Populations may be finite or infinite
 A population or collection of entities may however
be animals, machines, plants and cells or even
patients.

16. Populations and Parameters
• Population:
– A group of individuals that we would like to know something
about..
• Parameter:
– A characteristic of the population in which we have a
particular interest
• Often denoted with Greek letters (μ, σ, ρ)
• Examples:
– The proportion of the population that would respond to a certain
drug.
– The association between a risk factor and a disease in a
population.

17. Populations and Samples
• Studying populations is too expensive and time-
consuming, and thus impractical.
• If a sample is representative of the population, then by
observing the sample we can learn something about the
population.
– thus by looking at the characteristics of the sample (statistics),
we may learn something about the characteristics of the
population (parameters).

19. Statistical Analyses
• Two steps
– Descriptive Statistics
• Describe the sample
– Inferential statistics
• Make inferences about the population using what is
observed in the sample.
• Primarily performed in two ways:
– Hypothesis testing
– Estimation