1. Basics in Epidemiology & Biostatistics
Hashem Alhashemi MD, MPH, FRCPC
Assistant Professor, KSAU-HS

2. Objectives
• Definitions.
• Types of Data.
• Data summaries.
• Mean Χ , Stander deviation S.
• Stander Error SE, Confidence interval C.I of μ .

3. Epidemiology

4. Epidemiology

5. Biostatistics ??
What is the difference between the two?

6. Difference
 Biostatistics is application of statistical
methods in biology, medicine and public
health.
 Epidemiology is the study of patterns of
health and illness and associated factors
at the population level.

7. Descriptive Vs Inferential Statistics
• Descriptive: Range, mean, SD, Rank, median, IQR
Describe a data of a population or a sample.
• Inferential: Sample, SE, CI
Sample from a population, & trying to generalize
your finding (make an inference about the
population)

8. A Fancy World made of
Biostatistics
Averages & %s

9. Types of DATA
• Quantitative.
• Qualitative.

10. Quantitative
Data
Discrete
Continuous
Dichotomous:
Binary: Sex
Multichotomous:
1-No order : Race
2-Ordinal: Education
Numerical: number
pregnancies/residents
Ratio (real zero) /
Interval (no zero)
Temperature/BP
(Non-Parametric Data)

11. Quantitative
Data
Discrete
Continuous
Categorical :
1- Di-chotomous:
Sex
2- Multi-chotomous:
Race,Education
Numerical:
number of
pregnancies/residents
Ratio (real zero) /
Interval (no zero)
Temperature/BP
Types of
Data Count
Non-Parametric Data
Parametric Data
Parametric Data

12. Data Summaries??
Exams/memories
Understand/view

13. Summaries
Visual Numerical
X, 𝛍, s, 𝛔Histogram
P, 𝛑, s, 𝛔Bar & Pie Chart (Counts)
Categories
(Measures)
Any value

14. Data Presentation
%%

15. Controlled
PB ˂ 140/90
Non
adherence
(12 %)
Aadherence
(88 %)
(63 %)
(37 %)
Continuous Discrete
%%

16. A Fancy World made of
%s & Averages
Biostatistics

17. Normality & Approximation to Normality

18. Normality
Continuous Data Variability
Height= Mean X
Width= SD, SE
Central Tendency &
Dispersion

19. Binary Data approximation to Normality
Death Life

20. Coin: Head Vs Tail
Discrete: Two categories

22. Discrete Data
36 categories

27. Normality & Approximation to Normality
Why?

28. Approximation to Normality
• If choices are equally likely to happen
• If repeated numerous number of times
• It will look normal.
• Whether it was a coin or a dice
(Di-chotomous or Multi-chotomous)

29. Normality & Approximation to Normality
Clinical Relevance?

30. Choices equally likely to happen…..
i.e. Out come of interest probability is unknown
(Research ethics)
Repeated numerous number of times….
i.e. Large sample size
Normality assumption helps us predict
the Probability % of our outcome

31. The Bell / Normal curve
Stander deviation(SD)/ sample curve
True error (SE)/ population curve
• Was first discovered by Abraham de Moivre in 1733.
• The one who was able to reproduce it and identified
it as the normal distribution (error curve) was Gauss
in 1809.

32. De Moivre had hoped for a chair of
mathematics, but foreigners were at a
disadvantage, so although he was free
from religious discrimination, he still
suffered discrimination as a Frenchman in
England.
Born 1667 in Champagne, France
Died 1754 in London, England

33. • Large samples > 30.
• Normally distributed.
• Descriptive statistics:
Range, Mean, SD.
Non-parametric data
• For small samples & variables
that are not normally
distributed.
• No basic assumptions
(distribution free).
• Descriptive statistics:
Range, Rank, Median, & the
interquartile range.
(the middle 50 = Q3-Q1).
• Median is the middle number
in a ranked list of numbers.
Parametric data

34. The End

35. Why?

36. Different types of animals
Need different ways of care
Different types of Data
Summaries and analysis are different

37. Qualitative Studies/data
• Importance (Humanities):
needed when trying to find justifications,
explanations, opinions regarding the subject of
interest.
• Examples:
Emotions, Perceptions, Pictures.
• How:
Asking open ended questions (interviews),
observing behaviors….

38. Quantitative Studies/Data
• Importance (Science):
For measurements and/or estimation.
• Examples:
Measurable & countable data (real numbers).
• How:
Observation, Comparison, Intervention, Correlation.

39. BP ≥ 140/90
Uncontrolled
Controlled
PB ˂ 140/90
Non
adherence
(12 %)
Aadherence
(88 %)
(63 %)
(37 %)
Figure 2: Non-adherence to medications and blood pressure control.