PRESENTATION.pptx

PRESENTATION
MUHAMMAD DANIYAL
ASSISTANT DIRECTOR
RESEARCH CELL
RLMC

Biostatistics
• Biostatistics is the branch of statistics responsible for the
proper interpretation of scientific data generated in the
biology, public health and other health sciences (i.e., the
biomedical sciences)
• Biostatistics is the application of statistical methods to
Health Sciences

Variables
• Variable is a characteristic of a person, object
or phenomenon that can take on different
values. A simple example of a variable is a
person’s age. The variable age can take on
different values because a person can be 20
years old, 35 years old, and so on. .

TYPES OF VARIABLES
There are four types of variables
• Dependent Variable
• Independent Variable
• Qualitative Variable
• Quantitative Variable

DEPENDENT
AND
INDEPENDENT
VARIABLES
• Because in health system research you often
look for causal explanations, It is important
to make distinction between dependent and
independent variables. The variable that is
used to describe or measure the problem
under study (outcome) is called the
DEPENDENT variable. The variables that are
used to describe or measure the factors that
are assumed to cause or at least to influence
the problem are called the
INDEPENDENT(exposure) variables

Qualitative Variables and Quantitative
Variables
Qualitative Variables
•Qualitative Variables
mean Categorical
Variables
e.g., Gender, Job
Category, stages of
cancer etc.
Quantitative Variables
•Quantitative
Variables mean
numerical data
e.g., Age, amount of
Fat, weight of
patient, temperature

Types of Qualitative Variables
• There are two types Qualitative Variables
1.Nominal Variables
2.Ordinal Variables

Nominal Variables
• A nominal variable is another name for a categorical variable. Nominal variables have two or more
categories without having any kind of natural order. they are variables with no numeric value
• For Example:
• Nominal Data Categories
• Sex/Gender (Male, Female, Transgender).
• Eye color (Blue, Green, Brown, Hazel).
• Marital Status (Married, Single, Widowed, Divorced).
• Type of pet (Dog, Cat, Fish, Bird).

ORDINAL VARIABLE
• In ORDINAL VARIABLES, the variables are
also divided into a number of categories,
but they have natural order, from lowest to
highest or vice versa.
• Example:
• ORDINAL DATA
CATEGORIES
• Level of knowledge: good,
average, poor
• Level of blood pressure: high,
moderate, low

Types of
Quantitative
Variables
There are two types of
Quantitative Variables:
Discrete Variables
Continuous variables

Discrete Variables
• A discrete variable is a variable whose value is
obtained by a counting process and contain a
countable or a whole number.
• Examples: number of students.
• Examples: Number of Cars

Continuous
Variables
 Continuous variable — A variable that can
theoretically have infinite number of possible
values within a short range. Age is continuous
since within 8 and 12, it can be 8.17, 10.874,
9.756 years, etc. Age can be measured in
terms of days, hours and minutes, although
practically there is no need to do this. Blood
pressure is also a continuous variable.

Population
• The totality of individuals or units
of interest. For example, there
could be a population of blood
samples collected in a year. If the
interest is restricted to only
suspected cases of liver diseases, the
population comprises blood samples
of such cases only. If the interest is
further restricted to the cases
attending OPD in a group of
hospitals, the population is also
accordingly restricted.

Sample
• A set of data collected and/or
selected from a statistical
population. It is therefore a part
of a population obtained by a
defined procedure.

Parameter and
Statistic
• A statistic is a characteristic or
measure obtained by using the
data values from a sample.
• A parameter is a characteristic or
measure obtained by using the
data values from a specific
population.

Types Of Statistics
There are two
basic types of
statistics
Descriptive
Statistics
Inferential
Statistics

Descriptive
Statistics
• Summarize data using the measures of central
tendency, such as the mean, median, mode.
• Describe data using the measures of variation,
such as the range, variance, and standard
deviation.
• Identify the position of a data value in a data set
using various measures of position, such as
percentiles, deciles and quartiles.
• Use the techniques of exploratory data analysis,
including stem and leaf plots, box plots e.t.c

Inferential
statistics
• INFERENTIAL STATISTICS are certain types of
procedures that
• allow a researchers to make inferences
about a population
• based on findings from a sample
• Consists of generalizing from samples to
populations, performing estimations and
hypothesis tests, determining relationships
among variables, and making predictions.

Scale of
measurement
• N
• O
• I
• R

NOMINAL
SCALE
• Nominal scale deals with the non-numeric data
that is with the categorical data
• It is a system of assigning number to the
variable to label them only for identification and
to distinguish them from each
other. Example: Car-1, Buses-2
• It is a measure that simply divides objects or
events into categories
• It is considered as the weakest tool of the
measurement.
• It shows the quality of data.
• Here, categories are designated with names or
numerals but ordering of categories is
meaningless i.e. there is no order
• Examples: Gender, race, color preference, etc.

ORDINAL
SCALE
• It has unequal units
• It deals with qualitative data
• It displays from highest to lowest by
different measurement points
• Interval size is unequal and unknown
• Categories are distinct and
homogeneous
• They cannot be measured but can be
counted and ordered/ranked

SCALE OF
MEASUREMENT
Interval Scale
• In interval ration no absolute zero is exist
• For example : Temperature
Ratio Scale
• It is top level measurement of scale
• In ratio scale absolute zero is exist
• For example:
• sales figures, ruler measurements,
number of children. Speed of a car
before acceleration.

Summary Measures
Arithmetic Mean
Median
Mode
Describing Data Numerically
Variance
Standard Deviation
Coefficient of Variation
Range
Interquartile Range
Geometric Mean
Skewness
Central Tendency Variation Shape
Quartiles

Measures of Central
Tendency
• Measure of central tendency provides a very
convenient way of describing a set of scores
with a single number that describes the
PERFORMANCE of the group.
• It is also defined as a single value that is used
to describe the “center” of the data.
• There are three commonly used measures of
central tendency. These are the following:
• MEAN
• MEDIAN
• MODE

The Mean
(arithmetic average)
• The MEAN (or arithmetic mean) is
also known as the AVERAGE. It Is
calculated by totaling the results of
all the observations and dividing by
the total number of observations.
Note that the mean can only be
calculated for numerical data.

EXERCISE
• Find the mean of the following data:
• 12, 10,15, 10, 16, 12,10,15, 15, 13
• A. 13 B. 12.5 C. 15 D. 12.8
• Find the mean of the following data:
• 20, 24, 24, 24, 22, 22, 24, 22, 23, 25
• A. 23.5 B. 23 C. 24

The Median
• The MEDIAN is the value that
divides a distribution into two
equal halves. The median is useful
when some measurements are
much bigger or much smaller than
the rest. The mean of such data
will be biased toward these
extreme values. The median is not
influenced by extreme values.

The Median
Example
• The weights (in pounds) of seven army recruits
are 180, 201, 220, 191, 219, 209, and 186. Find
the median.
• Arrange the data in order and select the middle
point.
• There are different methods to calculate the
median.
• Median = n+1/2

The Median –
PRACTICAL
EXERCISE
• The ages of 10 college students are: 18,
24, 20, 35, 19, 23, 26, 23,
• 19, 20. Find the median.
• Find the median of the data:
• 5, 7, 4, 9, 5, 4, 4, 3
• Compute the Median/Mid Point

MODE
• The mode is the most frequently
occurring value in a set of
observations.
• A data set can have more than
one mode.
• A data set is said to have no
mode if all values occur with
equal frequency

MODE
• The following data represent the
duration (in days) of U.S. space
shuttle voyages for the years
1992-94. Find the mode.
• Data set: 8, 9, 9, 14, 8, 8, 10, 7, 6,
9, 7, 8, 10, 14, 11, 8, 14, 11.
• Ordered set: 6, 7, 7, 8, 8, 8, 8, 8,
9, 9, 9, 10, 10, 11, 11, 14, 14, 14.
Mode = 8.

The Mode
Examples
• Six strains of bacteria were tested
to see how long they could
remain alive outside their normal
environment. The time, in
minutes, is given below. Find the
mode.
• Data set: 2, 3, 5, 7, 8, 10.
• There is no mode since each data
value occurs equally with a
frequency of one.

The Mode
Examples
• Eleven different automobiles
were tested at a speed of 15 mph
for stopping distances. The
distance, in feet, is given below.
Find the mode.
• Data set: 15, 18, 18, 18, 20, 22,
24, 24, 24, 26, 26.
• There are two modes (bimodal).
The values are 18 and 24.

The Mode
PRACTICAL
EXAMPLES
• Find the mode of the following
data:
• 20, 14, 12, 14, 26, 16, 18, 19, 14
• A. 14 B. 17 C. 26 D. 16
• Find the mode of the following
data:
• 5, 0, 5, 4, 12, 2, 14
• A. 4 B. 5 C. 6 D. 0

Measures of Variation
same center,
different variation
Variation
Variance Standard
Deviation
Coefficient of
Variation
Range Interquartile
Range
 Measures of variation give
information on the spread or
variability of the data
values.

Range
 Simplest measure of variation
 Difference between the largest and the smallest values in a set of data:
Range = Xlargest – Xsmallest
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Range = 14 - 1 = 13
Example:

Interquartile Range
• Can eliminate some outlier problems by
using the interquartile range
• Eliminate some high- and low-valued
observations and calculate the range from
the remaining values
• Interquartile range = 3rd quartile – 1st
quartile
= Q3 – Q1

Box and Whisker Plot
5 - number summary
 Minimum
 first quartile Q1
 Median (Q2)
 third quartile Q3
 Maximum

Reveals the:
 center of the data
 spread of the data
 distribution of the data
 presence of outliers
Excellent for comparing two or more
data sets

Median
(Q2)
X
maximum
X
minimum
Q1 Q3
Example:
25% 25% 25% 25%
12 30 45 57 70
Interquartile range
= 57 – 30 = 27

• If the median is near the center of
the box, the distribution is
approximately symmetric.
• If the median falls to the left of the
center of the box, the distribution is
positively skewed.
• If the median falls to the right of
the center of the box, the
distribution is negatively skewed.

Quartiles
 Quartiles split the ranked data into 4 segments with an
equal number of values per segment
25% 25% 25% 25%
 The first quartile, Q1, is the value for which 25% of the
observations are smaller and 75% are larger
 Q2 is the same as the median (50% are smaller, 50% are
larger)
 Only 25% of the observations are greater than the third
quartile
Q1 Q2 Q3

Standard Deviation
• Most commonly used measure of variation
• Shows variation about the mean
• It is the square root of the variance
• It shows the dispersion or distance of the
scattered values from the mean under the
normal curve.

STANDARD DEVIATION
• The STANDARD DEVIATION is a measure, which describes how much
individual measurements differ from the mean. A large standard
deviation shows that there is a wide scatter of measured values
around the mean, while a small standard deviation shows that the
individual values are concentrated around the mean with little
variation among them

Measuring Variation
Small standard deviation
Large standard deviation

NORMAL DISTRIBUTION
• The commonest and the most useful continuous distribution.
• A symmetrical probability distribution where most results are located
in the middle and few are spread on both sides.
• The normal curve is bell-shaped and has a single peak at the exact
center of the distribution.
• The arithmetic mean, median, and mode of the distribution are equal
and located at the peak

PROPERTIES OF NORMAL DISTRIBUTION
• The mean, mode and median are all equal.
• The curve is symmetric at the center (i.e., around the mean, μ).
• Exactly half of the values are to the left of center and exactly half the
values are to the right.
• The total area under the curve is 1.
• It looks like bell shaped

The normal distribution
Mean = Median = Mode
A normal distribution is symmetric about its mean

Skewed distributions
• The data are not distributed symmetrically in skewed distributions
• Consequently, the mean, median, and mode are not equal and are in different
positions
• Scores are clustered at one end of the distribution
• A small number of extreme values are located in the limits of the opposite end
• Mean is highly affected by extreme values, so it is not best estimate skewed
distribution
• The median is a better estimate of skewed distributions

Shapes of distributions
Mode = Mean = Median
SKEWED LEFT
(negatively)
SYMMETRIC
Mean Mode
Median
SKEWED RIGHT
(positively)
Mean
Mode
Median

SYMMETRY &
SKEWNESS
1.Normal Distribution
Mean=Median=Mode
2. Positive Skewness
Mean>Median>Mode
3.Negative Skewness
Mean<Median<Mode

Outliers
• An outlier is an extremely high
or an extremely low data value
when compared with the rest of
the data values.
 A value located very far away
from almost all of the other
values
 An extreme value

Qualitative Data
(Categorical Variable)
Tables Graphs Numbers
One Way Table
Two Way Table
.
.
.
N Way Table
Bar Charts
Pie Charts
Clustered Bar
Charts
Percentages
Descriptive Analysis of Qualitative Data

Descriptive Analysis of Quantitative Data
Qualitative Data
(Numerical Data)
Tables Graphs Numbers
Frequency distribution
Steam & Leaf
Histogram & box plots
Center Important Points Variations Distributions
Mean
Median
Mode
Median
Quartile
Percentile
Ranges
Inter quartile
Ranges
Variance
Standard Deviation
Skewness
Kurtosis

Normality of Data
• Normality means
distribution of data
• Normality test are used to
determine the normality of
data

Normality Test
• Kolmogorov Simonov or
Shapiro wilk test is used to
determine the normality of
Data

Importance of Normality
Test
• Normality Test determine which
test would apply.
• If data follow Normal
distribution, then apply
Parametric Test
• If data do not follow the Normal
Distribution, then apply Non-
Parametric Test

SAMPLING
• A sample is a subset of the population,
with all its inherent qualities. Inferences
about the population can be made from
the measurements taken from a sample,
if the sample is truly representative of
the population.

SAMPLING
• Since a sample is expected to
represent the whole population,
the sampling procedure must
follow three fundamentals:
• Should be representative.
• Large enough.
• The selected elements should
have been properly approached.

REASONS FOR
USING
SAMPLES
• There are many good reasons for studying a sample
instead of an entire population:
• Samples can be studied more quickly than
populations. Speed can be important if a physician
needs to determine something quickly, such as a
vaccine or treatment for a new disease.
• A study of a sample is less expensive than a study of
an entire population because a smaller number of
items or subjects are examined. This consideration is
especially important in the design of large studies
that require a long follow-up.
• A study of the entire populations is impossible in
most situations.

STEPS IN SAMPLING
1. Definition of the population
• We first need to identify the population we wish to draw the
sample, from and do so some what formally because any
inferences we draw are only applicable to that population
2. Construction of a sampling frame(or thinking of an alternate)
• The list of all possible units that might be drawn in a sample.
3. Selection of a sampling procedure
• This is a critical decision about how to collect the sample. We
will look at some different sampling procedure in the following
slides.

TWO MAJOR TYPES OF SAMPLING PROCEDURES
PROBABILITY
• Each element has the same chance of being included in the sample. Major
types of probability sampling procedures:
• 1.Simple random
• 2.Systematic
• 3.Cluster
• 4.Stratified

TWO MAJOR TYPES OF SAMPLING PROCEDURES
NON-PROBABILITY
• There is no assurance that each element will have the same chance of
being included in the sample. The 3 major types:
• 1.Consecutive
• 2.Convenience
• 3.Purposive

WHY TESTING
HYPOTHESIS IS
IMPORTANT
• Hypothesis testing permits generalization of an
association or a difference obtained from a
sample to the population from which it came.
• Hypothesis testing involves conducting a test of
statistical significance and quantifying the degree
to which sampling variability may account for the
result observed in a particular study. It entails the
following steps.

HYPOTHESIS TESTING
• Hypothesis testing:
• What is a hypothesis?
• Why is a hypothesis needed ?
• What is the difference between hypothesis
and research question?
• What should come first?
• How can I change the research question into a
hypothesis?

HYPOTHESIS
• The word hypothesis consists of two words:
Hypo + thesis = Hypothesis. ‘Hypo’ means
tentative or subject to the verification and
‘Thesis’ means statement about solution of a
problem. The word meaning of the term
hypothesis (plural Hypotheses)is a tentative
statement about the solution of the problem.

WHY HYPOTHESIS IS
NEEDED
• A Hypothesis is needed as it offers a solution of the
problem that is to be verified. It is a brilliant guess about
the solution of a problem. A hypothesis is a tentative
statement about the relationship between two or more
variables. A hypothesis is a specific, testable prediction
about what you expect to happen in your study. To be
complete the hypothesis must include three
components: The variables; The population; and the
relationship between the variables.

DIFFERENCE
BETWEEN
RESEARCH
QUESTION
AND
HYPOTHESIS
• Research Question and hypothesis both are important,
research question help researcher to set the objectives and
hypothesis is derived from the research question. Hypothesis
is used mainly in quantitative research; research question and
hypothesis are the foundations of a research study.
A research question is a statement made in a question form
seeking to study, learn, explore, or examine more about the
research topic. It is aimed to focus on the research problem.
A research question would set boundaries for the area to be
explored and the answers that your research need to answer,
and hypothesis is a scientific way in which you assume an
answer to the research question or its sub-components and
then test if your assumption was correct.
• A research problem is about identifying something worth
researching.
• A research question is a question that focuses your study on
the research problem.
• A hypothesis is a guess at the results before you conduct
research. It should be something you can prove to be untrue.
• Finally, hypothesis will provide a solution about the problem.

HOW CAN WE CHANGE A RESEARCH QUESTION INTO A
HYPOTHESIS?
• We can change research question into hypothesis by making simple
statement.
• It is important to convert a research question into a concise and narrow
statement which should be measurable and testable.
• For example, a research question around COVID-19 transmission could be
‘How does SARS-CoV-2 jump from animal carriers to human carriers?’ The
research question typically leads to a hypothesis, a statement of a tentative
solution. There can be several research questions for a study or there can be
just one.
• Hypothesis: Outcome of SARS-COV-2 jump from animals to humans.
• Another Example:if you have a research question like “are men taller than
women on average?” you would measure the height of a group of men and a
group of women and propose two hypotheses. The first (the null hypothesis)
is that there is no difference in average heights between men and women.
The alternative would be that men on average are taller than women.

RESEARCH QUESTIONS
• What is difference in outcome of conventional dose regimen in comparison
to high dose of vancomycin among patients of bacterial meningitis?
• What is difference in outcome of zinc supplementation in comparison to
placebo in addition to standard therapy for management of children with
pneumonia?
• What maternal factors are associated with obesity in toddlers?
• How can siblings’ risk of depression be predicted after the death of a child?
• Is Zinc Supplementation better treatment to treat children suffering from
pneumonia than Standard treatment?

HYPOTHESIS?
• High dose vancomycin regimen has better outcome as compare to conventional dose of
vancomycin in acute bacterial meningitis patients
• Zinc Supplementation is better treatment to treat children suffering from pneumonia than
Standard treatment
• Maternal factors are associated with obesity in toddlers in Lahore/Punjab
• Prediction of risk of depression in siblings after the death of a child
• Zinc Supplementation is better treatment to treat children suffering from pneumonia than Standard
treatment.
• Topic: Comparison of Zinc Supplementation vs Placebo in addition to Standard Therapy for
management of children in pneumonia.

RESEARCH QUESTIONS
• How ants eat?
• Is Drug 23 an effective treatment for Disease A?
• What are the effects of sleep on reflexes?
• How do the students rate on critical thinking skills?
• What are the student’s achievement levels (or grades) in science classes?
• Does critical thinking ability relate to student achievement? (An inferential
question relating the independent and the dependent variables)
• Does smoking cause the lung cancer?

RESEARCH HYPOTHESIS
• Ants have teeth.
• Drug 23 will significantly reduce symptoms associated with Disease A
compared to Drug 22.
• Maximum reflex efficiency is achieved after eight hours of sleep.

EXAMPLES
• Research hypotheses are the research questions that drive the research
• e.g., lowering the fat in a person’s diet lowers the blood cholesterol levels
• e.g., better nutrition in childhood leads to increased adult height
• Suppose a study is being conducted to answer questions about differences
between two regimens for the management of diarrhea in children: the sugar
based modern ORS and the time-tested indigenous herbal solution made from
locally available herbs.
• One question that could be asked is: "In the population is there a difference in
overall improvement (after three days of treatment) between the ORS and the
herbal solution?”

Criteria for a
Good
Research
Question
F
I
N
E
R

FINER
• F – Feasible
• Adequate number of subjects
• Adequate technical expertise
• Affordable in time and money
• Manageable in scope
• I – Interesting
• Getting the answer intrigues investigator, peers and community
• N – Novel
• Confirms, refutes or extends previous findings
• E – Ethical
• Amenable to a study that institutional review board will approve
• R – Relevant
• To scientific knowledge
• To clinical and health policy
• To future research

EXERSICE
Write two sets of questions:
1-The first set should be descriptive questions about the independent and
dependent variables in the study.
2-The second set should pose questions that relate (or compare) the
independent variable(s) with the dependent variable(s).
This follows the models of descriptive and inferential questions.

TYPES OF HYPOTHESIS
1. Null Hypothesis
2. Alternative Hypothesis

NULL
HYPOTHESIS
• "There is no difference between the 2
regimens in term of improvement” (null
hypothesis).
• A null hypothesis is usually a statement that
there is no difference between groups or
that one factor is not dependent on another
and corresponds to the No answer.

ALTERNATIVE
HYPOTHESIS
• "There is a difference in terms of
improvement achieved by a three days
treatment with the ORS and that of the
herbal solution" (alternative hypothesis).
• Associated with the null hypothesis there is
always another hypothesis or implied
statement concerning the true relationship
among the variables or conditions under
study if no is an implausible answer. This
statement is called the alternative
hypothesis and corresponds to the “Yes”
answer.

EXAMPLE
• Example:
• Null Hypothesis: there is no difference between the two drugs on average
• Alternative Hypothesis: the two drugs have different effects, on average
• Statement: the new drug is better than the current drug, on average

CRITERIA FOR A GOOD HYPOTHESIS
• PICOT
• P Population (patients)
• What specific population are you interested in?
• I Intervention (for intervention studies only)
• What is your investigational intervention?
• C Comparison group
• What is the main alternative to compare with the intervention?
• O Outcome of interest
• What do you intend to accomplish, measure, improve or affect?
• T Time
• What is the appropriate follow-up time to assess outcome

STEPS IN HYPOTHESIS TESTING
1-Statement of research question in terms of statistical hypothesis (Null and
alternate hypothesis)
2-Selection of an appropriate level of significance. The significance level is
the risk we are willing to take that a sample which showed a difference was
misleading.5% significance level means that we are ready to take a 5%
chance of wrong results.

3-Choosing an appropriate statistics like t test, z test for continuous data, chi
square for proportions etc.
• Test statistics is computed from the sample data and is used to determine
whether the null hypothesis should be rejected or retained.
• Test statistics generates p value.

4-Performing calculations and obtaining p value.
5-Drawing conclusions, rejecting null hypothesis if the p value is less than the
set significance level.

• When we mistakenly reject the null when indeed the null is
true, then the type of wrong decision is known as a Type I or
Alpha error.
• However, when we mistakenly accept the null when, in fact, it is
false then we commit another error known as the Type II or Beta
error.

Outcomes and Probabilities
State of Nature
Decision
Do Not
Reject
H 0
No error Type II Error
( β )
Reject
H 0
Type I Error
( )
a
Possible Hypothesis Test Outcomes
H0 False
H0 True
No Error

• Beta error: is dependent on the
sample size.
• 1-beta is the Power of the
study which can detect as
different treatment as really can
be different.
• Power of the studies are
usually acceptable at 80% so
that there
• are only 20% chance of missing
the true differences.

P-VALUE
P value: P value is used in research to determine whether the sample size is really a
part of the population or not. The p-value is the probability that the observed
effect within the study would have occurred by chance if there was no true effect.
P-value is the evidence by which we can reject or accept the hypothesis.
• By convention, the p value is set at 0.05 level or <0.01. While some have debated
that the 0.05 level should be lowered, it is still universal. Thus, any value of p less
than or equal to 0.05 indicates that there is at most a 5% probability of observing
an association as large or larger than that found in the study due to chance alone
given that there is no association between exposure and outcome

P-VALUE
An example of findings reported with p values are below
Example:Drug 23 will significantly reduce symptoms associated with Disease A compared to
Drug 22.
Statement: Individuals who were prescribed Drug 23 experienced fewer symptoms (M = 1.3, SD =
0.7) compared to individuals who were prescribed Drug 22 (M = 5.3, SD = 1.9). This finding was
statistically significant, p= 0.02.
For this statement, the threshold has been set at 0.05, the null hypothesis stated (that there is no
statistical difference between the groups), and alternative hypothesis stated ( that there is statistical
difference between the groups), so the p-value is less than 0.05, we conclude that there is significant
difference between the groups, so we reject the null hypothesis, and we fail to reject the alternative
hypothesis which states that Drug 23 has better effect than Drug 22.

CONFIDENCE INTERVAL
• A confidence interval is a range of values within which it
is estimated with some confidence the population
parameter lies. The specified probability is called the
level of confidence

CONFIDENCE INTERVAL
• The confidence interval formula yields a range (interval) within
which we feel with some confidence the population mean is
located.
• It is not certain that the population mean is in the interval
unless we have a 100% confidence interval that is infinitely
wide, so wide that it is meaningless.

CONFIDENCE INTERVAL
• Common levels of confidence intervals used by analysts are
90%, 95%, 98%, and 99%.
• Most used confidence interval is the 95% interval
95% CI indicates that our estimated range has a 95% chance
of containing the true population value

PARAMETRIC TEST
Independent T-Test
Paired T TEST
ANOVA
Correlation

NON-PARAMETRIC
TEST
Mann Whitney U Test
Wilcoxon Signed Rank Test
Kruskal Wallis Test
Chi Square

CONTENTS
• WHAT IS CORRELATION?
• TYPE OF RELATIONSHIP AND STRENGTH OF
ASSOCIATION
• PROPERTIES OF CORRELATION

What is need of
CORRELATION?
• What happens to Sweater sales with increase in
temperature?
• What is the strength of association between
them?
• Ice-cream sales vs temperature ?
• What is the strength of association between
them?
• Which one of these two is stronger? How to
quantify the
association?

What is
CORRELATION?
• CO means two and RRELATION
means relationship
• Correlation is relationship between
two continuous variables

PROPERTIES OF
CORRELATION
• -1 ≤ r ≤ +1
• r=0 represents no linear relationship between the
two
Variables
• Correlation is unit free
Limitations:
• Though r measures how closely the two variables
approximate a straight line, it does not validly measure
the strength of nonlinear relationship
• When the sample size, n, is small we also have to be
careful with the reliability of the correlation
• Outliers could have a marked effect on r

STRENGTH OF ASSOCIATION
If “r” is close to “1” then there is strong correlation between the
variables and as we have “r” away from 1 then we have weak
correlation
If we have negative “r” value, then it means there is negative
correlation between the variables.

RESEARCH
A process of systematic, scientific data,
Collection
Analysis &
Interpretation
To find Solutions to a problem

• Re ---------------- Search
• Re means (once more, afresh, anew,
again
• Search means (go over thoroughly to look
something) OR (examine to find anything
new)
• Research is a systematic manner procedure

• To “learn something” and “to gather
evidence.”
• To remove any deficiency from previous
study
• To provide a solution to a problem
• A systematic way into a subject in order to
discover new facts

STEPS IN
DESIGNING
AND
CONDUCTING
RESEARCH
• Thinking about topic formulating research question/
objective
• Matching the Research Design to research
objectives
• Defining and clarifying the research Variables/
Analysis plan
• Drawing the Sample
• Developing the tools & defining the methods of data
collection
• Monitoring and Carrying out the research
• Preparing the Data for Analysis
• Analyzing Data
• Writing the Research Report

Research Cycle
New Questions Arise
Results Interpreted
Data Collected
Question Identified
Hypotheses Formed
Research Plan

Definition • Research is an organized and systematic way
of finding answers to questions.

CRITERIA FOR
SELECTING A
RESEARCH
TOPIC
• Relevance
• Innovation
• Feasibility
• Acceptability
• Cost-effectiveness
• Ethical consideration

OTHER CRITERIA
FOR SELECTING A
TOPIC
F
I
N
E
R

Literature
Search
• WHY?
• To keep up with the latest developments in
your field.
• To learn more about some topic.
• To document important facts and ideas you
wish to research in light of previous work
done on it.
• To understand your data in the context of
what is already known.
• To provide your readers with sources they
can consult on their own.

SEARCHING
SOURCES
• Journal Articles
• Search Engines (Google Scholar, PubMed,
Science Direct, Pakmedinet)
• Research Projects
• Conferences
• Researchers & Research Organizations

SEARCH
STRATEGY ON
INTERNET
Establish Establish the relationship between each
keyword and concept
Choose Choose appropriate keywords for each concept.
Identify Identify the unique ideas or concept associated
with your topic.
Summarize Summarize your topic in one or two sentences.

RESEARCH OBJECTIVE
An objective is an intent of
what the researcher wants to do
stated in clear measurable terms.”

Objectives
• Specific
• Measurable
• Attainable
• Realistic
• Time bound

IMPORTANCE OF RESEARCH
OBJECTIVES
Brings focus to the study.
Avoids collection of unnecessary data.
Determines an appropriate study design.
Helps determine analysis plan.

RESEARCH OBJECTIVE
A Good Objective ensures that:
What is to be measured is clearly stated, be
it a measure of frequency, Association or
comparison in the population of interest.

EXAMPLES
• Objectives:
• 1)To determine the frequency of anemia in pregnant women visiting
Tertiary care facilities of Sindh.
• 2) To determine association between maternal smoking and LBW.
• 3) To compare the effectiveness of dressing A vs. dressing B in patients
presenting with infected wounds of the foot.

Research Design
Definition
“Study design is the procedure under
which a study is carried out”

How to choose a Research
Design
• Does it adequately test the hypothesis?
• Does it identify and control the factors?
• Are results generalizable?
• Can the hypothesis be rejected or fail to reject via
statistical tests?
• Is the design efficient in using available
resources?

Cont……
• Level of Knowledge
• Nature of the research phenomenon
• Research purpose
• Ethical Considerations
• Feasibility
• Availability of subject
• Cost

STUDY DESIGNS
Epidemiological Study Designs
Analytical Studies
Observational
Studies
Experimental
Studies
Cohort
Case Control
RCT
Quasi
Descriptive Studies
Case Report
Case Series
Cross Sectional

Types of Studies
• Descriptive studies
describe occurrence of outcome
• Analytical studies
describe association between exposure and
outcome

DESCRIPTIVE STUDIES
• Descriptive studies involve the systematic
collection and presentation of data to
give a clear picture of a particular
situation and can be carried out on a
small or large scale.
• Case Report
• Case series
• Cross Sectional Survey

Descriptive Studies
• Case-Report
Detailed presentation of a single case
generally report a new or unique finding
these consist either of collections of reports on the
treatment of individual patients with the same
condition, or of reports on a single patient.

Example
• You have a patient that has a condition
that you are unfamiliar with. You would
search for case reports that could help
you decide on a direction of treatment
or to assist on a diagnosis

CASE REPORT
• A detailed report by a physician of an unusual disease in a single person.
• Classical example is that of a single case reported in Germany in late
1959 of a congenital malformation affecting the limbs and digits.
• More cases were reported in the following years. In 1961 a hypothesis
was put forward that thalidomide, a sleeping pill, was responsible for
congenital malformations.
• Subsequent analytic studies confirmed the link between the drug and
congenital malformation

Case-
Series
• Case-Series usually a
consecutive set of cases of a
disease with similar problem
which derive from the practice
of one or more healthcare
professionals.
• It is a group of patients with
similar conditions
• Cases may be identified from a
single or multiple sources
• Generally report on new/unique
condition

CASE SERIES
• When several unusual cases all with similar
conditions are described in a published report,
this is called a Case Series.
• A case series does not include a control group.

Cross-Sectional Study
• Classifies a population or group with respect to both outcome
and exposure at a single point in time
• It measures prevalence, not incidence of disease
• Also called “ Prevalence Studies”

CROSS SECTIONAL SURVEY
ADVANTAGES
• Fairly quick and easy to perform.
• Inexpensive.
• Useful for determining the prevalence of disease
for a defined population and can also measure
factors leading to it subsequent to group
formations

COMPERATIVE OR ANALYTICAL STUDIES
• An ANALYTICAL STUDY attempts to establish association or
determine risk factors for certain problems. This is done by
comparing two or more groups, with or without the outcome
of interest/exposure of interest.
Types
• Observational
• Experimental

Case-Control Study
• An “observational "design comparing exposures
in disease cases vs. healthy controls from same
population
• Exposure data collected retrospectively
• Most feasible design where disease outcomes are
rare

Case-Control Study
Cases: Disease
Controls: No disease

CASE CONTROL STUDY
• The investigator selects the case group and the
control group based on the outcome (i.e. having the
disease of interest vs. not having the disease of
interest)
• Cases and controls are assembled and are
questioned, or their medical records are consulted
regarding past exposure to risk factors

CASE CONTROL STUDY
• Advantages Inexpensive Quick Especially
useful when the disease being studied is
rare or for a condition which develops
over a long time Can evaluate multiple
etiologies for one outcome
• Disadvantages Recall bias Selection Bias

CASE CONTROL STUDY
Cases can be selected from a variety of sources:
• Hospital patients
• Patients in Physician’s practices
Controls may be selected from:
• Non-hospitalized persons living in the
community similar to cases.
• Hospitalized patients admitted for diseases
other than that for which cases are admitted

RECALL BIAS
• Individuals who have experienced a
disease or other adverse health events
tend to think about possible causes& thus
are likely to recall histories of exposure
differently as compared to controls.

COHORT STUDIES
• A cohort is a group of people who
have something in common (a
characteristic or characteristics
suspected of being a precursor to or
risk factor for a disease) and who
remain part of a group over a period.

Cohort Study
Prospective Study - looks forward, examines events in future,
follows a condition, concern or disease into the future
time
Study begins here

TYPES OF COHORT STUDIES
• Prospective Cohort Studies
• Retrospective Cohort Studies

PROSPECTIVE COHORT
STUDIES
• The investigator assembles the study groups
in the present time, collects baseline data
on them and then continues to collect data
for a period that can last many hours to
years.

RETROSPECTIVE COHORT
STUDY
• The investigator goes back into history to
define a risk group (e.g. those children
exposed to x-rays in utero vs. those not), and
follows the group members up to the present
to see what outcome (cancer) have occurred

PROSPECTIVE COHORT
STUDY:ADVANTAGES
Because they are longitudinal, are the study of choice
for:
• Establishing causes of a condition.
• Allows for measurement of incidence Study of
multiple effects of a single exposure.

PROSPECTIVE COHORT
STUDY:DISADVATAGES
• With diseases that develop over a long period of time, or with conditions
that occur as a result of long-standing exposure, many years are needed
and hence:
• High costs
• Long wait until results are obtained
• Loss to follow
• Are problematic when disease or outcome is rare. For example, studying
the risk factors/ clinical features associated with carcinoid tumors (very
slowly growing tumors).

RETROSPECTIVE COHORT
STUDY
• Advantages:
• Less expensive
• Completed in much shorter time than a prospective study
• Disadvantages:
• The quality of data collection is not as good, As records
generated for clinical purposes and not for research.
• Because of many biases associated with these studies,
carry less weight in establishing a cause than prospective
studies.

EXPERIMENTAL STUDY-RCT
• The researcher manipulates a situation and measures
the effects of the manipulation amongst two groups,
one in which the intervention takes place (e.g.
treatment with a certain drug)and another group that
remains "untouched" (e.g., treatment with a placebo).
• RCT is Gold Standard Study.

EXPERIMENTAL STUDY
• Only type of study design that can prove causation.
• Individuals are randomly allocated to at least two
groups. One group is subjected to an intervention,
while the other group is not.
• The outcome of the intervention (effect of the
intervention on the dependent variable)is obtained by
comparing the two groups.

CHARACTERISTICS OF
EXPERIMENTAL STUDY
• Assignment of exposure (intervention) by the
researcher
• An intervention and a comparative group
• Random allocation
Techniques
• Single Blind technique.
• Double Blind technique.
• Triple Blind Technique.

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