This document provides an introduction to biostatistics. It outlines several key objectives of a biostatistics course including understanding descriptive statistics, statistical inference, common tests and their assumptions. It defines important statistical concepts like population, sample, parameters, statistics, variables, and types of statistical analysis. Descriptive statistics are used to summarize data, while inferential statistics allow generalizing from samples to populations. Examples of potential statistical abuses are also provided.

1. Introduction to Biostatistics
Dr Ali Al Mousawi

2. What are Statistics? Y= X1a + X2b +X3c + e

3. Why You Should Care
•Without knowledge of statistics, you are lost.
•It’s on the test.

5. 5
Why you should be here
•you have an interest in statistical reasoning
•you have a desire to learn to use statistics properly in experimental design and data analysis
•you want to develop your ability to critically assess scientific (or pseudo-scientific) arguments

6. 6
Objectives of Biostatistics course
•Understand the fundamental principles of Descriptive statistics.
•Understand the fundamental principles of statistical inference.
•Understand the general principles underlying the most common tests.
•Know the assumptions of common tests and understand impact of violations.
•Be able to perform standard statistical analyses.

8. You can:
•provide objective criteria for evaluating hypotheses
•synthesize information (not without information loss… keep your raw data!)
•help detect patterns in messy data
•help optimize effort
•help you critically evaluate arguments

9. You can’t:
•tell the truth (probabilistic conclusions only!)
•compensate for poor design
•indicate biological significance: statistical significance does not mean biological significance, nor vice versa!

10. Statistics definition
•Statistics is a science that deals with the methods of collection, organization, analysis, interpretation, and presentation of information that can be stated numerically.
•Biostatistics is simply statistics as applied to the biological sciences, health, and medicine.
•Note: statistics is used to describe also specific numbers (sample data)

11. Statistics
 Method of analysis
a collection of methods for planning experiments, obtaining data, and then then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data

12. Statistics
Specific number
numerical measurement determined by a set of data (sample)
Example: Twenty-three percent of people polled believed that there are too many polls.

13. Uses of Statistics
Almost all fields of study benefit from the application of statistical methods

14. The uses of statistics:
•To collect and use empirical data efficiently to gain the most value with the least cost
•To use empirical data to describe the world around us.
•Interpretation of data
•To use empirical data to understand the world around us
•To characterize replicable processes
•To distinguish random noise from pattern

15. Deductive and Inductive reasoning
•Deductive reasoning: general principles are applied to the specific situation at hand in order to reach the best decision possible for a particular patient. This type of reasoning – from the general to the specific –
•Inductive reasoning: We study a few patients (or experimental animals), and from what we observe. we try to make rational inferences about what happens in general. This type of reasoning – from the specific subject(s) at hand to the general –

16. Definitions
•Parameter: a numerical measurement describing some characteristic of a population
•Statistic: a numerical measurement describing some characteristic of a sample
Population Sample
Parameter Statistic

17. Sample vs. Population
Population
Sample

18. Parameters and Statistics
- Experimenters normally use sample statistics as estimates of population parameters.
-Population parameters are written with Greek letters; sample statistics with Latin letters.

20. Two main types of Statistics:
• Descriptive Statistics
summarize or describe the important characteristics of a known set of population data
• Inferential Statistics
use sample data to make inferences (or generalizations) about a population

21. Descriptive statistics
•Collection, organization, summarization, and presentation of data.
•Descriptive statistics are used to describe the main features of a collection of data in quantitative terms.
–Descriptive statistics aim to quantitatively summarize a data set
•Some statistical summaries are especially common in descriptive analyses. For example
–Frequency Distribution
–Central Tendency
–Dispersion
–Association

22. Descriptive Statistics
•Descriptive Statistics are used by researchers to report on Populations and Samples
•Population
The complete collection of all elements (scores, people, measurements, and so on) to be studied.
-Census
the collection of data from every element in a population
•Sample:
a subgroup or subset of elements drawn from a population

23. •Raw Data: Data collected in original form
•In statistics, a variable has two defining characteristics:
•A variable is an attribute that describes a person, place, thing, or idea.
•The value of the variable can "vary" from one entity to another.
•Random Variable: A variable whose values are determined by chance. A random variable can be thought of as an unknown value that may change every time it is inspected.
Definitions

24. Definitions
•Dependent or response or out come variable: a variable that depend on other variable/s.
•Independent or predictor or explanatory variable: variable that does not depend on other variable/s.
•Confounding variable: variable/s that correlates (directly or inversely) with both the dependent variable and the independent variable.

25. .
Dependent variable
Independent variable
Confounding variable

26. Inferential statistics
Inferential statistics: Used to make an inference, on the basis of data, about the (non)existence of a relationship between the independent and dependent variables.

27. Inferential Statistics
Generalizing from samples to populations using probabilities. Performing hypothesis testing, determining relationships between variables, and making predictions.

28. Bias
In survey sampling, bias refers to the tendency of a sample statistic to systematically over- or under-estimate a population parameter, types:
Selection bias-Nonresponse bias
-Under-coverage bias
-Voluntary response bias
Measurement bias-Leading questions
-Social desirability

29. Misuse of statistical analysis
•Obsession with statistical recipes, in particular, hypothesis
testing – “Pavlov’s dog” reaction to any hypothesis derived
from data, demanding statistical significance test;
•Use of statistical techniques as a “black-box”, or cook-book
recipe (standard example is disregard of serial correlation).
•Misunderstanding or misinterpreting the names (e.g.
decorrelation time, p-values as probability of hypotheses)
•Use of sophisticated techniques... There is sometimes
unwarranted expectation of miracle-like results from very
advanced techniques.

30. Abuses od Statistics
•Bad Samples
• Small Sample
• Loaded Questions
• Misleading Graphs
•Pictographs
•Partial pictures
•Distorted percentages
•deliberate distortion

33. Bachelor High School Degree Diploma
Figure 1-1 Salaries of People with Bachelor’s Degrees and with High School Diplomas
$40,000
30,000
25,000
20,000
$40,500
$24,400
35,000
$40,000
20,000
10,000
0
$40,500
$24,400
30,000
Bachelor High School Degree Diploma
(a)
(b)

34. Important Note
We should analyze the numerical information given in the graph instead of being mislead by its general shape.

35. Double the length, width, and height of a cube, and the volume increases by a factor of eight

36. “Ninety percent of all our cars sold in this country in the last 10 years are still on the road.”

37. Abuses of Statistics
•Bad Samples
self-selected survey
(or voluntary response sample)
one in which the respondents themselves decide whether to be included

38. Hey!
Do you believe
in the death
penalty?
Convenience Sampling - use results that are readily available

39. THANK YOU