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4 Principles of Experimental Design

Youngkyu , Hyun Woo, Jennifer, Se June. 4 Principles of Experimental Design. 4 Principles. 1. 2. 4. 3. Control. Blocking. Replication. Randomize. Experimental Design. 1. Control. 4 Principles of Experimental Design. Control. What do we Control?

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4 Principles of Experimental Design

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  1. Youngkyu, Hyun Woo, Jennifer, Se June 4 Principles of Experimental Design

  2. 4 Principles 1 2 4 3 Control Blocking Replication Randomize Experimental Design

  3. 1 Control 4 Principles of Experimental Design

  4. Control What do we Control? - both the experimental factors and other sources of variation How do we Control? - making conditions as similar as possible all treatment groups - assigning subjects to different factor levels - reduces the variability of responses -> easier to detect differences - prevent the sources of variation from changing/affecting the response variable - increase the efficiency of an experimental design by decreasing the experimental error - but! too much generalization can be risky

  5. 2 Randomize 4 Principles of Experimental Design

  6. Randomize allows us to equalize the effects of unknown or uncontrollable sources of variation. If experimental units were not assigned to treatments at random, we would not be able to use the powerful methods of Statistics to draw conclusions from an experiment. reduces bias, even from those we don’t realize. “control what you can, and randomize the rest.” Methods of Randomization:Flipping a coinDrawing out a number from a pool of numbersUsing a computer program

  7. 3 Replication 4 Principles of Experimental Design

  8. Replication • 2 different types of replication • 1. Apply to many different subjects -Assess the variability of responses -one is an experience, to many is data • 2. Replication of whole experiment -Controlled sources of variation at different levels -Ex) Volunteers in Korea not representative

  9. 4 Blocking 4 Principles of Experimental Design

  10. Blocking Blocking itself is not exactly about blocking. Blocking is used to make distribution of the sample fairer because... - there may be variables that we cannot control that may affect the outcome of our experiment By grouping similar individuals together (by like age) and then randomizing them within those groups, or blocks, we can narrow down our results into a more representative data. It’s not necessary.

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