Designing experiments would always be simple if we could just randomly assign subjects to different treatment conditions with no other restrictions. Unfortunately, that doesn’t always work.
For example, there are many experimental situations where the subjects aren’t independent of each other. The subjects that are related to each other are combined into clusters called “blocks.” It can happen due to practicalities of running an experiment efficiently or you can intentionally plan it as a way to reduce random variance.
In either case, this is a randomized complete block design. It’s a great design to become familiar with because it will greatly expand your ability to create and analyze experiments.
How It Works
When you have subjects that share characteristics with one another, it can sometimes be difficult to isolate those characteristics directly. This makes it hard to record them as additional variables. By identifying the subjects that are similar, you can still capture how those characteristics affect the outcome. Subjects that are similar are grouped into “blocks.”
From there, you can make treatment assignments so that you put subjects from the same block into different treatment groups.
Why different treatment groups? Suppose subjects from the same block were assigned to the same treatment group. (more…)
In this webinar, we’ll discuss when tables and graphs are (and are not) appropriate and how people engage with each of these media.
Then we’ll discuss design principles for good tables and graphs and review examples that meet these principles. Finally, we’ll show that the choice between tables and graphs is not always dichotomous: tables can be incorporated into graphs and vice versa.
Participants will learn how to bring more thoughtfulness to the process of deciding when to use tables and when to use graphs in their work. They will also learn about design principles and examples they can adopt to create better tables and graphs.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
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This webinar, presented by Yasamin Miller, will cover broadly survey design and planning.
It will outline the advantages and disadvantages of the various data collection modes, types of samples available to target your population, how to obtain a representative sample, and how to avoid the pitfalls of bad questionnaire design.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
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