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📚 Answering Questions with Data
Chapters ▾
0 Preface1 Why Statistics?2 Describing Data3 Correlation4 Probability, Sampling, and Estimation5 Foundations for inference6 t-tests7 ANOVA8 Repeated Measures ANOVA9 Factorial ANOVA10 More On Factorial Designs11 Simulating Data12 Thinking about answering questions with data13 GIFs14 References

12.3 Planning your design

Our discussion of effect size and power highlight the importance of the understanding the statistical limitations of an experimental design. In particular, we have seen the relationship between:

  1. Sample-size
  2. Effect-size
  3. Alpha criterion
  4. Power

As a general rule of thumb, small N designs can only reliably detect very large effects, whereas large N designs can reliably detect much smaller effects. As a researcher, it is your responsibility to plan your design accordingly so that it is capable of reliably detecting the kinds of effects it is intended to measure.

Adapted from Answering Questions with Data: Introductory Statistics for Psychology Students, by Matthew J. C. Crump (Brooklyn College of CUNY), crumplab.com/statistics, licensed under CC BY-SA 4.0. Portions adapt Danielle Navarro's Learning Statistics with R (CC BY-SA). Changes were made; this adaptation is distributed under the same license. License: CC-BY-SA-4.0.