Statistics Idiots Guide!
Dr. Hamda Qotba, B.Med.Sc,
M.D, ABCM
Definition
Statistics is the science of collecting,
organizing, summarising, analysing,
and making inference from data
Descriptive stat. Includes
collecting, organizing,
summarising, analysing,
and presenting data
Inferential stat. Includes
Making inferences,
hypothesis testing
Determining relationship,
and making prediction
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Variables
Quantitative
•Discrete
•Continuous
Qualitative
•Ordinal
•Categorical
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Parametric Vs.
non parametric tests
• Parametric: decision making method
where the distribution of the
sampling statistic is known
• Non-Parametric: decision making
method which does not require
knowledge of the distribution of the
sampling statistic
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t-Test
• Compare the means of a continuous
variable into samples in order to
determine whether or not the
difference between the 2 expected
means exceed the difference that
would be expected by chance
What is probability the mean will differ?
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Requirements
• The observations are independent
• Drawn from normally distributed
population
• Sample size < 30 if it’s >30 use
normal curve z test (binomial test)
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Types of t-Test
• One sample t test: test if a sample mean
for a variable differs significantly from the
given population with a known mean
• Unpaired or independent t test: test if the
population means estimated by
independent 2 samples differ significantly
(group of male and group of female)
• Paired t test: test if the population means
estimated by dependent samples differ
significantly (mean of pre and post
treatment for sameDr.H.Qotba
set of patients
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chi² test
• Used to test strength of association
between qualitative variables
• Used for categorical data
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Requirements
• Data should be in form of frequency
• Total number of observed must exceed 20
• Expected frequency in one category or in
any cell must be >5 (When 1 of the cells have <5
in observed yats correction) or if (When 1 of the cells
have <5 in expected fischer exact)
• The group compared must be
approximately the same
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Correlation and Regression
• Methods to study magnitude of the
association and the functional
relationship between two or more
variables
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Correlation
• Denote strength of relationship between variables
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Regression
• Method that’s indicate a mathematical
relationship between a dependant and
one or more independent variables
• Simple linear regression and multiple
regression are appropriate for
continuous variables like(BP, Weight)
• Logistic regression applicable for
binary response like alive/dead
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Measures
• If parametric
• Pearson correlation coeff.
»Continuous variables
»Linear relationship
• If nonparametric
• Spearman rank
»Both variables are continuous
• Kendall’s tau
»Two ordinal or one ordinal one
continuous
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ANOVA
• is used to uncover the main and
interaction effects of categorical
independent variables (called "factors")
on an interval dependent variable
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Types of ANOVA
• One-way ANOVA tests differences in
a single interval dependent variable
among two, three, or more groups
formed by the categories of a single
categorical independent variable.
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• Two-way ANOVA analyzes one interval
dependent in terms of the categories
(groups) formed by two independents,
one of which may be conceived as a
control variable
• Multivariate or n-way ANOVA. To
generalize, n-way ANOVA deals with n
independents. It should be noted that as
the number of independents increases,
the number of potential interactions
proliferates
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How to select appropriate
statistical test
• Type of variables
• Quantitative (blood pres.)
• Qualitative (gender)
• Type of research question
• Association
• Comparison
• Risk factor
• Data structure
• Independent
• Paired
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Body of research question
Association of 2 variable(dep, indep)
Types of variable
Dependent
independent
Test
categorical
categorical
chi-square
categorical
Quantitative
Log. regression
Quantitative
categorical
2 out come T test
3+out come ANOVA
Quantitative
Quantitative
Spearman Correlation
linear Regression
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Comparing (difference) variables
Variable
Number of independent variable
2 groups
paired data
>2groups
Quantitative
T test
Ordinal
MannWhitney
Paired T test
Categorical chi-square*
ANOVA
Wilcoxon
Kruskal
wallis
McNemar
chi-square
* When 1 of the cells have <5 in expected fischer exact
When 1 of the cells have <5 in observed yats correction
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Looking for Risk Factor
Types of variables
Test
Dependent
several indepen.
categorical
categorical
Multiple log.
Regression
quantitative
categorical
ANOVA
quantitative
quantitative
Linear, log
regression
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