Chapter 1 biostat

Larson & Farber, Elementary Statistics: Picturing the World, 3e 1

Biostatistics
course hand out

Course content
• Chapter one introduces you to the study of statistics
generally. You are given the definition and led to how
Biostatistics as an entity is derived.
• The main types of statistics are discussed as well as common
terms encountered in the course of studying the subject, such
as variables and their types, population, samples etc.
• Also this chapter introduces scales of measurement of
numerical data which will help you to understand how data
could be classified for easy and meaningful presentations.
Scales as nominal, ordinal, interval and ratio are introduced in
this unit.

• In the second chapter, we will learn the basic step in
organizing the data. You will learn about the need to
arrange data according to their magnitude in ascending or
descending order as the case may be.(i.e. from the lowest
to the highest or vice versa).
• You will also learn about frequency distribution to help
you understand how frequent certain variables occur in a
given series.
• This chapter also introduces the concept of range and
practical application of Biostatistics to health care
delivery.

• In the third chapter, we will look at the measure of central
tendency, the single value that attempts to describe a set
of data by identifying the central position within that set of
data. Measures of central tendency are sometimes called
measures of central location. They are also classed as
summary statistics.
• In this chapter we will take three most common measures
of central tendency. The mean (often called the average) is
most likely the measure of central tendency that you are
most familiar with, but there are others, such as the median
and the mode.
•

• The mean, median and mode are all valid measures of
central tendency, but under different conditions, some
measures of central tendency become more appropriate to
use than others.
• Therefore we will look at the mean, mode and median, and
learn how to calculate them and under what conditions
they are most appropriate to be used

• In chapter four, we will look measures of variation or
dispersion. In the previous chapters we will take a look for
several measures which are used to describe the central
tendency of a distribution were considered. While the
mean, median, etc. give useful information about the
center of the data, We also need to know how “spread
out” the numbers are about the center.
• The reason of measuring this scatter or dispersion is to
obtain a single summary figure which adequately exhibits
whether the distribution is compact or spread out.

• Hence it is essential to know how far these observations
are scattered from each other or from the mean. Like
different measures of central tendency, we will also loo the
different measures of variation.
• Some of the commonly used measures of dispersion
(variation) that we will learn are: Range, interquartile range,
variance, standard deviation and coefficient of variation.

Chapter one
Introduction to Statistics

Introduction
• Most people familiar with statistics through radio,
televisions, and newspapers and magazines.
• The following statements are statements that we hear
every day
• A vitamins and mineral pill boosted certain immune
response in older people by 64%.
• 95% of Somaliland community are un employed
• Every five children born in sub-Saharan countries two of
them was dead for their first months.
• 90% of Somali mother deliver their babies in the homes

• Statistics is used in all fields of human endeavour.
• In sport: for example, a statistician may keep records of
the number yards a running back and front during
football game (3450 km)
• In public health: the number of human been dead due to
cancer for the last decade.
• In education: the researcher might want to know if new
methods of teaching are better than old ones.
• These are only a few examples of how statistics can be
used in various occupations.

definition of statistics
Statistics is a field of study concerned with
1- collection, organization, summarization and analysis of data.
2- drawing of inferences about a body of data when only a part of
the data is observed.
3-interpret and communicate the results to others.

Biostatistics
• The tools of statistics are employed in many fields: business,
education, psychology, agriculture, economics, … etc.
• Therefore, When the data analyzed are derived from the biological
science and medicine we call biostatistics , to distinguish this
particular application of statistical tools and concepts from any
other tools.

Data and Statistics
Data consists of information coming from observations,
counts, measurements, or responses.
Statistics is the science of collecting, organizing, analyzing,
and interpreting data in order to make decisions.
A population is the collection of all outcomes, responses,
measurement, or counts that are of interest.
A sample is a subset of a population.

Populations & Samples
Example:
In a recent survey, 250 college students at Union College
were asked if they smoked cigarettes regularly. 35 of the
students said yes. Identify the population and the sample.
Responses of all students at
Union College (population)
Responses of students
in survey (sample)

Parameters & Statistics
A parameter is a numerical description of a population
characteristic.
A statistic is a numerical description of a sample
characteristic.
Parameter Population
Statistic Sample

Branches of Statistics
The study of statistics has two major branches: descriptive
statistics and inferential statistics.
Statistics
Descriptive
statistics
Inferential
statistics
Involves the
organization,
summarization,
and display of data.
Involves using a
sample to draw
conclusions about a
population.

Data
• The raw material of Statistics is data.
• Data are the values that the variable can assume, data can be
as figures. 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 weighs a patient (measurement)

• We search for suitable data to serve as the raw material for our
investigation.
• Such data are available from one or more of the following
sources:
Internal sources (Routinely kept records.
• For example:
• - Hospital medical records contain immense amounts of
information on patients.
- Hospital accounting records contain a wealth of data on the
facility’s Hospital activities.
Sources of Data

2- External sources.
The data we need may 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.

Surveys:
• The source may be a survey, if the data needed is about answering
certain questions.
For example:
• If the administrator of a clinic wishes to obtain information regarding
the mode of transportation used by patients to visit the clinic, then a
survey may be conducted among patients to obtain this information.

• Experiments.
• Frequently the data needed to answer a question are available only
as the result of an experiment.
• For example:
• If a nurse wishes to know which of several strategies is best for
maximizing patient compliance, she might conduct an experiment in
which the different strategies of motivating compliance are tried
with different patients.

Types of Data
Data sets can consist of two types of data: qualitative data
and quantitative data.
Data
Qualitative
Data
Quantitative
Data
Consists of
attributes, labels,
or nonnumerical
entries.
Consists of
numerical
measurements or
counts.

Qualitative and Quantitative Data
Example:
The grade point averages of five students are listed in the
table. Which data are qualitative data and which are
quantitative data?
Student GPA
Sally 3.22
Bob 3.98
Cindy 2.75
Mark 2.24
Kathy 3.84
Quantitative dataQualitative data

Levels of Measurement
The level of measurement determines which statistical
calculations are meaningful. The four levels of
measurement are: nominal, ordinal, interval, and ratio.
Levels
of
Measurement
Nominal
Ordinal
Interval
Ratio
Lowest
to
highest

Nominal Level of Measurement
Data at the nominal level of measurement are qualitative
only.
Levels
of
Measurement
Nominal
Calculated using names, labels,
or qualities. No mathematical
computations can be made at
this level.
Colors in
the US
flag
Names of
students in your
class
Textbooks you
are using this
semester

Ordinal Level of Measurement
Data at the ordinal level of measurement are qualitative
or quantitative.
Levels
of
Measurement Arranged in order, but
differences between data
entries are not meaningful.
Class standings:
freshman,
sophomore,
junior, senior
Numbers on the
back of each
player’s shirt
Ordinal
Top 50 songs
played on the
radio

Interval Level of Measurement
Data at the interval level of measurement are quantitative.
A zero entry simply represents a position on a scale; the
entry is not an inherent zero.
Levels
of
Measurement
Arranged in order, the differences
between data entries can be calculated.
Temperatures Years on a
timeline
Interval
Atlanta Braves
World Series
victories

Ratio Level of Measurement
Data at the ratio level of measurement are similar to the
interval level, but a zero entry is meaningful.
Levels
of
Measurement
A ratio of two data values can be
formed so one data value can be
expressed as a ratio.
Ages Grade point
averages
Ratio
Weights

Summary of Levels of Measurement
NoNoNoYesNominal
NoNoYesYesOrdinal
NoYesYesYesInterval
YesYesYesYesRatio
Determine if
one data value
is a multiple of
another
Subtract
data values
Arrange
data in
order
Put data
in
categories
Level of
measurement

Methods of Data Collection
In an observational study, a researcher observes and
measures characteristics of interest of part of a population.
In an experiment, a treatment is applied to part of a
population, and responses are observed.
A simulation is the use of a mathematical or physical model
to reproduce the conditions of a situation or process.
A survey is an investigation of one or more characteristics
of a population.
A census is a measurement of an entire population.
A sampling is a measurement of part of a population.

Sampling techniques
A stratified sample has members from each segment of a
population. This ensures that each segment from the
population is represented.
Freshmen Sophomores Juniors Seniors

A cluster sample has all members from randomly selected
segments of a population. This is used when the population
falls into naturally occurring subgroups.
The city of Clarksville divided into city blocks.
All members
in each
selected group
are used.

A systematic sample is a sample in which each member of
the population is assigned a number. A starting number is
randomly selected and sample members are selected at
regular intervals.
Every fourth member is chosen.

A convenience sample consists only of available members
of the population.
Example:
You are doing a study to determine the number of years of
education each teacher at your college has. Identify the sampling
technique used if you select the samples listed.
1.) You randomly select two different departments and survey each
teacher in those departments.
2.) You select only the teachers you currently have this semester.
3.) You divide the teachers up according to their department and
then choose and survey some teachers in each department. Continued.

Identifying the Sampling Technique
Example continued:
You are doing a study to determine the number of years of
education each teacher at your college has. Identify the sampling
technique used if you select the samples listed.
1.) This is a cluster sample because each department is a naturally
occurring subdivision.
2.) This is a convenience sample because you are using the teachers
that are readily available to you.
3.) This is a stratified sample because the teachers are divided by
department and some from each department are randomly
selected.

Chapter 1 biostat

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