1. Course outlines
• Introduction to medical statistics
1. Definitions
2. Types of data and Variables
3. Summarizing data
• Measures of central tendency and dispersion
• Tests for continuous data
• Tests for categorical data
• Distribution curves –probability and significance
• Sampling – sample size
• Vital statistics
2. This session
• Introduction to medical statistics
1. Definitions
2. Types of data and Variables
• Uses of statistics
4. • The word biostatistics.
• Bio, which comes from the Greek: bios= life.
• Statistics, originally came from the same root
as latin: stato = state. A state or community.
5. • To make a story with numbers - more precise.
• (example: observed child deaths in certain
community!! How much? How many? Is the
number of deaths large enough to alarm or
small to normal level) so number tells the
story
• Biostatistics is really statistics focusing on
health issues
6. • Statistical thinking will one day be as
necessary for efficient citizenship as the
ability to read and write (Samuel Wilks
1906-1964).
7. • The first book written on statistics was actually a book
written on biostatistics by John Graunt in the middle of
the 17th century.
• The basis of this book: 100 years ago they had been
collecting statistics in London:
1. Every week from each parish
2. The number of deaths
3. The cause of those deaths why were they doing this?
Because the plague was running around.
What could they do about the plague? Nothing, except
run away from it
So every week they would come out with these statistics
8. • Persons who could afford to buy the book,
they look at the statistics. If the number of
plague related deaths is going up they would
pack their bags and run a way and wait until
the wave died down.
• John Graunt looked at his statistics and made
a story
• Example AIDS: how many people are living
with HIV, how many people just got infected
in the last year with HIV, how many people
died from AIDS in the last year
We can keep tabs of what is going on
9. Rift Valley fever in Mauritania
(Situation as of 30 October 2012)
• 1 November 2012 - The Ministry of Health (MoH)
in Mauritania declared an outbreak of Rift Valley
Fever (RVF) on 4 October 2012. From 16
September to 30 October 2012, a total of 34
cases, including 17 deaths have been reported
from 6 regions. The last case was notified on the
27 October 2012 from Magta Lahjar in the Brakna
region. The 6 regions include Assaba, Brakna,
Hodh Chargui, Hodh Gharbi, Tagant and Trarza.
All the cases had history of contact with animals.
10. Terminologies
Data
• Are the basic building units (or blocks ) of statistics.
• Data consist of discrete observations of attributes
or events
• Data has little meaning when considered alone .
• Data need to be transferred into information by
reducing them , summarizing them and adjusting
them for variations (e.g: age and sex composition of
the population)
• So that comparisons over time and place are
possible .
11. Terminologies
• Statistics is the study of how to collect,
organize, analyze, and interpret numerical
information from data
• Biostatistics—the theory and techniques for
collecting, describing, analyzing, and
interpreting health data.
12. Terminologies
• Population refer to all measurements or
observations of interest in reference or
universe
• Sample is simply a part of the population. But
the sample MUST represent the population.
– A random sample is such a representative sample
• The sample must be large enough
• The sample should be selected randomly
13. Terminologies
• Parameter is some numerical or nominal
characteristic of a population
– A parameter is constant, e.g. mean of a population
– Usually unknown
• Statistic is some numerical or nominal characteristic
of a sample.
– We use statistic as an estimate of a parameter of the
population
– It tends to differ from one sample to another
– We also use statistic to test hypothesis
14. • How we do estimates from the numbers or
data? The course of biostatistics
• The three basics of statistics are:
1. Variability: making sense from variation
2. Inference: making generalization
3. Probability: making proportion and chance
15. Variables
Is a characteristic which varies within the units
Example 1: Persons in homogenous population( Age ,
Sex, City of birth, Socio-economic status etc)
Example 2: Physical environment( light, noise, ionizing
radiation, housing, etc)
Example 3: malaria control( use of impregnated bed
nets, use of skin repellants, covering water barrels,
break the mosquito breeding cycle etc)
16. Variable – Data Relation
Data
Variable
18 ys or 20 ys or 50 ys………ect
Age
Male, female
Sex
Khartoum, Omdurman
City
Low, moderate, high
Socio-economic status
illumination, glare
light
Intensity, frequency
Noise
During night only, at sun set and
through out night, for children, for
pregnant women
Use of impregnated bed nets
Clean air condition once a week,
empty the water barrel on each
Friday
Break the mosquito breeding cycle
17. Types of Data and Variables
1. Qualitative
2. Quantitative
18. Qualitative Data
• Are not numerical
• Usually names. (sex: male or female, city of birth:
Khartoum or Omdurman)
• Are also called nominal, categorical or attribute
variables.
• In the special case where a variable assumes two
values only (e.g. alive/dead) it is called a binary,
dichotomous variable or binomial data.
• Some qualitative variables also have an intrinsic
order; ordinal variables (e.g. socio-economic group
1 and group ll .Mild , Moderate and severe
malnutrition)
Attribute: A quality or characteristic
inherent in or ascribed to someone or
something.
19. Categorical Variables
• Nominal (unordered): male/female, alive/ dead,
blood group 0, A, B, AB(the order does not
matter).
• Ordinal (ordered): grade of breast cancer, agree-
neither agree nor disagree- disagree(the order
does matter).
• Quantitative continuous data can be transferred
to:
1. Categorical nominal( normotensive,
hypertensive- hypotensive)
2. Ordinal ( tall- average-short)
20. • Categorizing data is therefore useful for
summarizing results, but not for statistical
analysis
• These definitions of types of data are not
unique, nor are they mutually exclusive, and
are given as an aid to help an investigator
decide how to display and analyze data.
23. Uses of Statistics
• Data presentation
• Simplifies large numbers of figures and
reduces volume of data
• Enables comparisons across different groups
• Helps us to form and test hypotheses
• Helps in prediction, planning and
administration
• Helps form suitable policies
• Helps measure standard of health
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