A lecture of biostatistics mainly designed for medical students

1. Biostatistics
Dr. Hamdi Abdulwahab Alhakimi
Albaha University
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2. By the end of these session, attendants should:
• Understand the basic concepts of biostatistics.
• Differentiate between descriptive and
inferential biostatistics.
• Clearly identify types of data.
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3. List of contents
• Statistics: definitions and uses.
• Data: types and distributions.
• Descriptive statistics: calculations, tabulations &
graphs.
• Normal distribution: characteristics & uses.
• Inferential statistic: hypothesis testing &
estimation.
• How to choose appropriate statistical
procedures?
• Interpretation of statistics used in scientific
papers.
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4. Statistics
• The doing of statistics is a way of thinking about
numbers (collection, analysis, and presentation),
with intention to relate their meaning to the
objectives for which they are collected.
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5. • Formulas are only a part of that thinking,
simply tools of the trade; they are needed but
not as the only things one needs to know.
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6. Biostatistics
Basically, a successful research should consist of:
(1) Good research question.
(2) Investigation (Calibration & Sampling).
(3) Presentation of results (descriptive & inferential
statistics).
(4)Conclusions (inferential statistics).
the last three elements; together they form a
field called biostatistics.
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7. DATA
RESULTS
+
conclusions
Biostatistics
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8. Biostatistics
Descriptive
Statistics
Inferential
statistics
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9. Biostatistics
Descriptive
Statistics
graphstabulationscalculations
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10. Descriptive
Statistics
graphstabulations
calculations
- Proportions, rates &
ratios.
- Measures of central
tendency (Mean,
Mode & Median).
- Measures of
dispersion (S.d,
range).
-correlation coefficient
- Regression
coefficient.
- Quantiles.
-Frequency
distribution
tables.
-Cross tabs.
- stem & leaf
diagram.
- Bar graphs.
-Pie chart.
-Histogram.
- Scatter plot.
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11. Biostatistics
Inferential
statistics
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12. Inferential
statistics
Hypothesis
Testing
Estimation
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13. Inferential
statistics
Hypothesis
Testing
Estimation
• Standard Error
Confidence Interval
• Significance Tests
P value
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14. Important Note:
“A poor or invalid statistical analysis can be
repeated using correct methods but no
amount of data manipulation can
compensate for invalid data.”
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15. • Data are set of observations, measurements
or counts …..ect which have not meaning
alone.
• Data >>>>> Information >>>>> Intelligence
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16. DATA
Dichotomous
Polychotomous
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17. Types of Variables
• (Quantitative) Numerical variables:
– Always numbers
– Examples: age in years, weight, blood pressure readings,
temperature, concentrations of pollutants and, counts of
cases per week other measurements
• (qualitative) Categorical variables:
– Information that can be found into categories
– Types of categorical variables – ordinal, nominal and
dichotomous (binary)

18. Categorical Variables:
Nominal Variables
• Nominal variable – a categorical variable without an
intrinsic order
• Examples of nominal variables:
– Residence (Northeast, South, Midwest, etc.)
– Sex (male, female)
– Nationality (American, Mexican, French)
– Race/ethnicity (African American, Hispanic, White, Asian
American)
– Favorite pet (dog, cat, fish, snake)

19. Categorical Variables:
Dichotomous Variables
• Dichotomous (or binary) variables – a categorical
variable with only 2 levels of categories
– Often represents the answer to a yes or no question
• For example:
– “Did you attend the church on May 24?”
– “Did you eat potato salad ?”
– Anything with only 2 categories

20. Categorical Variables:
Ordinal Variables
• Ordinal variable—a categorical variable with some
intrinsic order
• Examples of ordinal variables:
– Education (no high school degree, HS degree, some
college, college degree)
– Agreement (strongly disagree, disagree, neutral, agree,
strongly agree)
– Rating (excellent, good, fair, poor)
– Frequency (always, often, sometimes, never)

21. Question:
• If we conducted a research and collected a
tremendous amount of data how could we
deal with these data, in order to present
results and draw conclusions???
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22. Answer
• You can use descriptive statistics to present
the findings of study according to the type of
data.
• You can use inferential statistics to draw
conclusions.
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23. Use of descriptive
Statistics in
qualitative data
graphs
tabulations
calculations
- Proportions, rates &
ratios.
-Frequency
distribution
tables.
-Cross tabs.
- Bar graphs.
-Pie chart.
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24. Descriptive statistics in qualitative data
• Proportions:
percentages, specificity, sensitivity …ect.
• Rates:
prevalence, incidence, mortality, morbidity ,
fatality rates, change rates …ect.
• Ratios:
- odds ratio.
- relative risk.
- S.M.R
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25. Use of descriptive
Statistics in quantitative
graphs
calculations
- Measures of central
tendency (Mean,
Mode & Median).
- Measures of
dispersion (S.d,
range).
-correlation coefficient
- Regression
coefficient.
- Quintiles.
- Histogram.
- Scatter plot.
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26. Description of Quantitative Data
- If normally distributed Mean & S.d.
- If not normally distributed Median&range.
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27. Story of standard deviation
• Average deviation = SUM(X.-Mean)/n
• Variance = SUM(X.-Mean)2/n-1
• Standard Deviation = square root of variance.
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28. Distribution of Data
“The way in which the observations distribute
themselves over the range of possible values.”
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29. • Fortunately, a small number of Distributions tend to occur
frequently;
- Binomial Distribution.
- Poisson Distribution.
- Student t-distribution.
- Normal Distribution.
- Skewed Distributions.
- Log-distribution.
- Bimodal Distribution.
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30. DATA
Quantitative
Continuous
- Normal
Distribution
- Non-normal
Distributions
Discrete
Poisson Distribution
Qualitative
Dichotomous
Binomial
Distribution
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31. WHY NORMAL DISTRIBUTION?
Because:
- For a large sample the binomial distribution
approximates to a normal distribution.
-The Poisson distribution approximates to a normal
distribution with mean=variance.
- Non-normal distributions (skewed & log) could be
transformed into normal distribution.
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32. Normalizing Transformation
• Normalizing transformations are a very powerful
weapon in the statistical armory.
- positively skewed distribution by taking
square root of each observation.
- Log distribution by taking the natural
logarithm of each observation.
- Negatively skewed by reciprocation.
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33. Properties of Normal Distribution:
- Bell-shaped symmetrical around the mean.
- Totally described by its mean & standard deviation.
- Mean=Mode=Median.
- 68.2% of observations lie within 1 standard deviation.
- 95% of observations lie within 1.96 (or tow) standard
deviations.
- 99.9% of observations lie within 3 standard deviations.
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34. Question
• You conducted a research about the
association between smoking & blood
pressure. How do you know that raw data you
collect about blood pressure are normally
distributed???
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35. Answer
• Doing a histogram for data about blood
pressure by using SPSS.
• Using statistical procedures like:
“Siminirov kilomigrov”
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36. Inferential Statistics
Questions:
• Is the descriptive statistics enough?
• What is the additional benefit of inferential
statistics over the descriptive statistics??
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37. Example:
• If the results if your research were:
“the relative risk of smoker to be hypertensive
is 3 times greater than non-smoker”
Is that enough to say the smoking will increase
the risk of hypertension?
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38. Answer:
• Inferential statistics enable us to do
generalization of our descriptive statistics.
• Inferential statistics make us able to say that
our results are not due to chance.
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39. Inferential
statistics
Hypothesis
Testing
Estimation
• Standard Error
Confidence Interval
• Significance Tests
P value
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40. Hypothesis Testing
(Basic Concepts)
• Statistics are findings calculated from a
sample.
• Parameters are their population-based
counterparts; these are unknown values.
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41. Hypothesis Testing
(Basic Concepts)
• If a sample is representative of a population, a
statistics will actually give similar but not the
same parameters of population.
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42. Hypothesis Testing
(Basic Concepts)
• Representative sample means it:
- reflects the actual variation present in
population by random sampling.
- is big enough by calculation of sample size.
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43. Hypothesis Testing
(Basic Concepts)
• Hypotheses: we have two statements about
the population;
• Null hypothesis -- hypothesis of no difference
or no effect.
• Alternative hypothesis -- Research
hypothesis; hypothesis of a difference or
effect.
• Note: we use parameters of population here.
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44. Hypothesis Testing
(Basic Concepts)
“The P-value is the probability of the observed
data or more extreme outcome would have
occurred by chance alone if the null
hypothesis is true.”
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45. Hypothesis Testing
(Basic Concepts)
• Level of significance is a cut-off point to which
a P-value is judged; usually 0.05 or 0.01.
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46. Steps of Hypothesis Testing
1- Statement of null & alternative hypotheses.
2- Determination of the level of significance.
3- Choosing of appropriate significance test and
calculation of “test statistic.”
4- Calculation of “P-value.”
5- Making decision on accepting or rejecting null
hypothesis.
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47. Decision rule
• P-value < significance level:
- rejecting null hypothesis.
- results are significant.
- results does not occur by chance.
• P-value > or = significance level:
- not rejecting null hypothesis.
- results are not significant.
- results occur by chance.
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