M.

SANI
Q1.

Biostatistics is a branch of statistics that applies statistical methods and principles to biological, medical,
and health-related research. It involves the design of biological experiments, the collection and analysis
of data from those experiments, and the interpretation of the results.

Importance of Biostatistics in Health Research:

1. Study Design: Biostatistics helps in planning and designing health studies, including clinical trials,
observational studies, and surveys, ensuring the methods are scientifically sound.
2. Data Analysis: It provides tools to analyze complex medical data, allowing researchers to make
sense of trends, relationships, and patterns within health data.
3. Evidence-Based Decisions: It supports evidence-based medicine by quantifying the effects of
treatments, risks of diseases, and outcomes, helping healthcare providers make informed
decisions.
4. Public Health Monitoring: Biostatistical methods are used to track disease outbreaks, monitor
public health trends, and evaluate the effectiveness of health programs.
5. Genetics and Epidemiology: It plays a key role in understanding the genetic basis of diseases
and the spread of diseases in populations.
6. Improving Patient Care: By analyzing clinical data, biostatistics contributes to improving
diagnosis, treatment effectiveness, and patient outcomes.

In short, biostatistics is essential for transforming health data into meaningful information that drives
progress in medicine and public health.

Q2.

Definition of Quantitative Data

Quantitative Data, as the name suggests is one which deals with and quantity or numbers. It is refers to
the data which computes the values and counts and can be expressed in numerical terms is called
quantitative data. In statistics, most of the analysis are conducted using this data.

Quantitative data may be used in computation and statistical test. It's concerned with measurements
like height, weight, volume, length, size, humidity, speed, age etc. The tabular and diagrammatic
presentation of data is also possible, in the form of charts, graphs, tables, etc. Further, the quantitative
data can be classified as discrete or continuous data.

The methods used for the collection of data are:

 Surveys
 Experiments
 Observations and Interviews

Definition of Qualitative Data

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Qualitative Data refers to the data that provides insights and understanding about a particular problem.
It can be approximated but cannot be computed. Hence, the researcher should possess complete
knowledge about the type of characteristic. Prior to the collection of data.

The nature of data is descriptive and so it is a bit difficult to analyze it.

This type of data can be classified into categories, on the basis of physical attributes and properties of
the object. The data is interpreted as spoken or written narratives rather than numbers. It is concerned
with the data that is observable in terms of smell, appearance, taste, feel, texture, gender, nationality
and so on. The methods of collecting qualitative data are:

 Focus Group
 Observation
 Interviews
 Archival Materials like newspapers.

Q3

Sure! Here's a clear difference between sample and population in statistics:

POPULATION:

It refers to the entire group that you want to study or draw conclusions about. It includes all members
or all possible outcomes related to the topic of interest.

Example: If you are studying the height of students in a university, the population is all students in the
university.

SAMPLE:

It is a subset of the population, meaning it includes only some members chosen to represent the whole
population. We study the sample because it’s often too difficult, expensive, or time-consuming to study
the entire population.

Example: Selecting 200 students from the university to measure their heights is a sample.

In short:

Population = Whole group

Sample = Part of the group

Q4

1. NOMINAL SCALE
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Nominal Scale is a scale of measurement used to label or categorize data without

any inherent order or numerical value. It's used to group data in to distinct
categories and there are no any mathematical operations can be performed.

The numerical examples of Nominal scale are:

a. Colour e.g red, blue and green etc.

b. Religion e.g Islamic, Christianity, Hindu etc
c. Product codes e.g product A(101), product B,(202), product C(303) etc.

2. ORDINAL SCALE

An ordinal scale is a scale of measurement that rank data in order, but the
interval between the consecutive levels are not equal. It shows a natural order or
sequence, but the differences between levels are not Quantifiable.

The numerical examples of ordinal scale are:

a. Educational level e.g high school, bachelor degree, master's degree, doctor
of philosophy (PHD) etc.
b. Customer satisfaction e.g satisfied, dissatisfied, neutral etc.
c. Ranking e.g 1st place, 2nd place, 3rd place etc.

3) INTERVAL SCALE: is a scale of measurement that has equal interval between

consecutive levels and allows for meaningful addition and subtraction e.g 1-2, 2-3,
3-4 etc.

The numerical examples of interval scale are:

a) Temperature (Celsius) e.g 20°c, 25°c, 30°c, ( equal interval of 50°c).

b) Calendar of the year e.g 2020, 2021, 2022 ( equal interval of 1 year).

c) Test scores e.g 70, 80, 90, 100 ( which has equal interval of 10 ).

4) RATIO SCALE: is a scale of measurement that has equal interval between

consecutive levels and allows for meaningful addition, subtraction, multiplication
and division.
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The numerical examples of ratio scale are:

1) Weight ( kilograms) e.g 10kg, 20kg, 30kg which has equal interval through zero
(0).

2) Height ( meters) e.g 1.5m, 2.0m, 2.5m

3) Age (years) e.g 20yrs, 30yrs, 40yrs which has equal interval through zero (0).

4) Time ( second ) e.g 10sec, 20sec, 30sec which has equal interval through zero
(0).
Q5

Definition

A measure of central tendency (also referred to as measures of centre or central location) is a summary
measure that attempts to describe a whole set of data with a single value that represents the middle or
centre of its distribution.

There are three main measures of central tendency:

 mode
 median
 mean

Each of these measures describes a different indication of the typical or central value in the distribution.

Mode

The mode is the most commonly occurring value in a distribution.

Consider this dataset showing the retirement age of 11 people, in whole years:

54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60

This table shows a simple frequency distribution of the retirement age data

The most commonly occurring value is 54, therefore the mode of this distribution is 54 years.

Median

The median is the middle value in distribution when the values are arranged in ascending or descending
order.

Mean
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The mean is the sum of the value of each observation in a dataset divided by the number of
observations. This is also known as the arithmetic average.

Looking at the retirement age distribution again:

54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60

The mean is calculated by adding together all the values (54+54+54+55+56+57+57+58+58+60+60 = 623)
and dividing by the number of observations (11) which equals 56.6 years.