Week 5 - Lectures Chs09_Part2.pptx - SCI1020 Introduction to statistical reasoning Part 2 How Data is Obtained Topic Introduction to Experimental design

Week 5 - Lectures Chs09_Part2.pptx - SCI1020 Introduction...

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SCI1020 Introduction to statistical reasoning Part 2: How Data is Obtained Topic: Introduction to Experimental design Ref: Moore et al, Chapter 9 Lectures prepared by Dianne Atkinson
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WPUML 2 Attendance Code
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. II. Experiment - Terminology explanatory variable /s (also called a factor/s ) and measured response variable/s . Possible values of a factors are called levels . A treatment is the combination of specific levels from all the factors that an experimental unit receives. A single individual or object being measured is called an experimental unit . Randomized experiments: Participants are randomly assigned to participate in one condition (called treatment ) or another. 3
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. Experimental Design – Critical Features of any experiment 1. Control. Use methods to control effects of confounding & lurking variables on the response. 2. Randomization. Assign experimental units to treatments by chance to create treatment groups that have similar backgrounds. Reduce bias and balance out lurking variables across treatments. 3. Replication. Repeat each treatment on many units to minimize influence of chance error = sample size in each treatment group The number of replicates will be decided by the requirements of precision in the study and by time and budget constraints. 4
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. Designing an Experiment Recall: Purpose of an experiment is to study whether the treatment causes a change in the response. Now, variation in the response could be a result of: explanatory variables (this is WHAT we want) – control confounding variables (effects need to be minimized) - control lurking variables (effects need to be minimized) - control chance error (effects need to be minimized) – randomisation, replication 5
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Experimental Design – 1. RANDOMISATION RANDOMISATION is the random allocation of experimental units among the treatment groups 6 Essential feature of any good experiment Assists comparison and cause-and-effect conclusion as the background extraneous variables will probably have a very similar distribution across all treatment groups before we apply the treatments. This similar background is more likely if there are many units (subjects) in each treatment group 
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Experimental Design 2. REPLICATION REPLICATION involves repeating each treatment on a large enough number of units to allow systematic differences between the treatments to be seen. 7 Without replication real treatment effects cannot be distinguished from uncontrolled variation in response or random error from ‘strange’ individual units.
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