1. Areej Jouhar & Hafsa El-Zain 2015-2016 Biostatistics BIOS 101 Foundation year

2. GRADE DISTRIBUTION 2

3. Resources Book: Biostatistics Basic Concepts and Methodology for Health Sciences. Online book: http://onlinestatbook.com/2/summarizing_distributions /measures.html http://onlinestatbook.com/2/summarizing_distributions /measures.html Online book: http://www.statisticallysignificantconsulting.com/Statis tics101.htm http://www.statisticallysignificantconsulting.com/Statis tics101.htm 3

4. Course Contents Lecture 1,2 and 3 – Introduction to Biostatistics and types of data variables. – Data collection methods. – Frequency distributions – Present data in form of graphically and numerical Lecture 4,5 and 6 – Calculate and interpret Central trendy Measures – Calculate and interpret Variation Measures. – Introduction to Sampling distribution 4

5. Course Contents Lecture 7 and 8 – Inferential statistics probability distributions – Inferential statistics normal distributions Lecture 9 – Central limit theorem 5

6. Outlines Learning objectives Why we learn statistics Variables Population and samples Tools of statistics  Descriptive statistics  Inferential statistics Data types  Qualitative data  Quantitative data Data measurements levels Summary 6

7. Learning Objectives After completing this Lecture, you should be able to: Know key definitions: Population vs. Sample What we mean by variables ( dependent vs. independent). Qualitative vs. Quantitative data Explain the difference between descriptive and inferential statistics Understand how to categorize data by type and level of measurement. 7

8. What Is The Statistic Statistic is a field of study concerned with : Collection, organization, summarization data The drawing of inferences of a huge quantity of data when only a sample of the data is examined 8

9. Why We Learn Statistic Statistic provides a way of organizing data to get information on wider and more formal (objective) basis than relying on personal experience (subjective). 9

10. Variables variables are anything that might change from one to another ( students marks, kids heights ) EXAMPLE : Math mark of students it will change from student to another,,, Blood group of patients (O+, O-, B+, B-, A+, A-, AB+, AB-),,, students ages 10

11. Populations and Samples A Population is the set of all items or individuals of interest Examples: All registered patients of specific disease A Sample is a subset of the population Examples: A few patients selected for dental testing Every 100 th receipt selected for audit 11

12. Tools Of Statistics Descriptive statistics Collecting, presenting, and describing data Inferential statistics Drawing conclusions and/or making decisions concerning a population based only on sample data 12

13. Descriptive Statistics Collect data  e.g., Survey, Observation, Experiments Present data  e.g., Charts and graphs Characterize data  e.g., Sample mean = 13

14. Inferential Statistics Making statements about a population by examining sample results Sample statistics Population parameters (known) Inference (unknown, but can be estimated from sample evidence) 14

15. Inferential Statistics Estimation  e.g., Estimate the population mean age using the sample mean age Hypothesis Testing  e.g., Use sample evidence to test the state that the population mean age is 54 years Drawing conclusions and/or making decisions concerning a population based on sample results. 15

16. Examples: Number of Children Number of students absent Examples: Weight Age Examples: Academic grades Clothing size(small, medium, large) Examples: Marital Status Medical Specialist Tooth Stain Color Data Types Quantitative Qualitative 16

17. 17 Data Types

18. Qualitative Data Nominal: Nominal data have no order and thus only gives names or labels to various categories. Ordinal : Ordinal data have order, but the interval between measurements is not meaningful. 18

19. Data Measurement Levels Ratio/Interval Data Ordinal Data Nominal Data Highest Level Complete Analysis Higher Level Mid-level Analysis Lowest Level Basic Analysis  Categorical Codes ID Numbers  Category Names  Rankings  Ordered Categories 19

20.  Reviewed key data collection methods  Introduced key definitions: Population vs. Sample Qualitative vs. Quantitative data  Examined descriptive vs. inferential statistics  Reviewed data types and measurement levels Summary 20

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