Chapter 1 Introduction to Statistics 1 41 5

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Chapter 1 Introduction to Statistics 1 -4/1. 5 Collecting Sample Data Copyright © 2014,

Chapter 1 Introduction to Statistics 1 -4/1. 5 Collecting Sample Data Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -1

Basics of Collecting Data Statistical methods are driven by the data that we collect.

Basics of Collecting Data Statistical methods are driven by the data that we collect. We typically obtain data from two distinct sources: observational studies and experiment. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -2

Observational Study v Observational study observing and measuring specific characteristics without attempting to modify

Observational Study v Observational study observing and measuring specific characteristics without attempting to modify the subjects being studied. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -3

Experiment v Experiment apply some treatment and then observe its effects on the subjects

Experiment v Experiment apply some treatment and then observe its effects on the subjects (subjects in experiments are called experimental units) Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -4

Example v The Pew Research Center surveyed 2252 adults and found that 59% of

Example v The Pew Research Center surveyed 2252 adults and found that 59% of them go online wirelessly. v This an observational study because the adults had no treatment applied to them. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -5

Example v In the largest public health experiment ever conducted, 200, 745 children were

Example v In the largest public health experiment ever conducted, 200, 745 children were given the Salk vaccine, while another 201, 229 children were given a placebo. v The vaccine injections constitute a treatment that modified the subjects, so this is an example of an experiment. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -6

Simple Random Sample v Simple Random Sample A sample of n subjects is selected

Simple Random Sample v Simple Random Sample A sample of n subjects is selected in such a way that every possible sample of the same size n has the same chance of being chosen. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -7

Simple Random Sample Choose 4 students from a class. Ex 1 Row 1 G

Simple Random Sample Choose 4 students from a class. Ex 1 Row 1 G 2 G 3 pick 2 Row 2 B 1 B 2 B 3 pick 2 (not possible to pick G 1, G 2, G 3, B 1) Ex 2 1 group G 1 B 1 G 2 B 2 G 3 B 3 pick 4 (all combinations of 4 are possible) Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -8

Sampling Methods v Random v Systematic v Convenience v Stratified v Cluster v Multistage

Sampling Methods v Random v Systematic v Convenience v Stratified v Cluster v Multistage Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -9

Random Sample v Random Sample Members from the population are selected in such a

Random Sample v Random Sample Members from the population are selected in such a way that each individual member in the population has an equal chance of being selected. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -10

Systematic Sampling Select some starting point and then select every kth element in the

Systematic Sampling Select some starting point and then select every kth element in the population. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -11

Convenience Sampling Use results that are easy to get. Copyright © 2014, 2012, 2010

Convenience Sampling Use results that are easy to get. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -12

Stratified Sampling Subdivide the population into at least two different subgroups that share the

Stratified Sampling Subdivide the population into at least two different subgroups that share the same characteristics, then draw a sample from each subgroup (or stratum). Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -13

Cluster Sampling Divide the population area into sections (or clusters). Then randomly select some

Cluster Sampling Divide the population area into sections (or clusters). Then randomly select some of those clusters. Now choose all members from selected clusters. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -14

Multistage Sampling Collect data by using some combination of the basic sampling methods. In

Multistage Sampling Collect data by using some combination of the basic sampling methods. In a multistage sample design, pollsters select a sample in different stages, and each stage might use different methods of sampling. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -15

Design of Experiments v Randomization is used when subjects are assigned to different groups

Design of Experiments v Randomization is used when subjects are assigned to different groups through a process of random selection. The logic is to use chance as a way to create two groups that are similar. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -16

Design of Experiments v Blinding is a technique in which the subject doesn’t know

Design of Experiments v Blinding is a technique in which the subject doesn’t know whether he or she is receiving a treatment or a placebo. Blinding allows us to determine whether the treatment effect is significantly different from a placebo effect, which occurs when an untreated subject reports improvement in symptoms. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -17

Design of Experiments v Double-Blinding occurs at two levels: (1) The subject doesn’t know

Design of Experiments v Double-Blinding occurs at two levels: (1) The subject doesn’t know whether he or she is receiving the treatment or a placebo. (2) The experimenter does not know whether he or she is administering the treatment or placebo. Copyright © 2014, 2012, 2010 Pearson Education, Inc. Section 1. 4 -18