Areej Jouhar & Hafsa El-Zain 2015-2016

Biostatistics BIOS 101

Lecture #1 INTRODUCTION TO

BIOSTATISTICS

Foundation year

GRADE DISTRIBUTION

Grade :

Subject :

10 Quizzes

10 First Assessment exam

10 Second Assessment exam

20 Practical exam

10 Self learning (Project)

40 Final exam

100 Total 2

Resources

• Book: Biostatistics Basic Concepts and Methodology for Health Sciences.

• Online book: http://onlinestatbook.com/2/summarizing_distributions/measures.html

• Online book: http://www.statisticallysignificantconsulting.com/Statistics101.htm

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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

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Course Contents

• Lecture 7 and 8– Inferential statistics probability distributions – Inferential statistics normal distributions

• Lecture 9 – Central limit theorem

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Outlines

Learning objectivesWhy we learn statisticsVariablesPopulation and samplesTools of statistics

Descriptive statisticsInferential statistics

Data typesQualitative dataQuantitative data

Data measurements levelsSummary 6

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 statisticsUnderstand how to categorize data by type and level of

measurement.7

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

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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).

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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

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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

100th receipt selected for audit

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Tools Of Statistics

Descriptive statisticsCollecting, presenting, and describing

data

Inferential statisticsDrawing conclusions and/or making

decisions concerning a population based only on sample data

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Descriptive Statistics Collect data

e.g., Survey, Observation,

Experiments

Present data e.g., Charts and graphs

Characterize data e.g., Sample mean =

n

x i

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Inferential Statistics

Sample Population

•Making statements about a population by examining sample results

Sample statistics Population parameters (known) Inference (unknown, but can

be estimated from sample evidence)

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Inferential Statistics

Estimatione.g., Estimate the population

mean age using the sample mean age

Hypothesis Testinge.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.

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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

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Qualitative Data Quantitative Data•Deals with descriptions.

•Data can be observed but not measured.

•Colors, textures, smells, tastes, appearance, beauty, etc.

• Deals with numbers.

•Data which can be measured.

•Length, height, area, volume, weight, speed, time, temperature, humidity, sound levels, cost, members, ages, etc.

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Data Types

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.

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Data Measurement Levels

Ratio/Interval Data

Ordinal Data

Nominal Data

Highest Level

Complete Analysis

Higher LevelMid-level Analysis

Lowest Level

Basic Analysis

Categorical Codes ID Numbers

Category Names

Rankings

Ordered Categories

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Reviewed key data collection methodsIntroduced key definitions:• Population vs. Sample

• Qualitative vs. Quantitative data

Examined descriptive vs. inferential statistics

Reviewed data types and measurement levels

Summary

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