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Experimental Design in Python
Learn about experimental design, and how to explore your data to ask and answer meaningful questions.
- 4 hours
- 16 Videos
- 53 Exercises
- 737 Participants
- 4,400 XP
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Course Description
Data is all around us and can help us to understand many things. Making a pretty graph is great, but how can we tell the difference between a few outliers on a graph and a real, reliable effect? Is a trend that we see on a graph a reliable result or just random chance playing tricks? In this course, you will learn how to interrogate datasets in a rigorous way, giving clear answers to your questions. You will learn a range of statistical tests, how to apply them, how to understand their results, and how to deal with their shortcomings. Along the way, you will explore Olympic athlete data and the differences between populations of continents.
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1
The Basics of Statistical Hypothesis Testing
FreeIn this chapter, you will learn how to explore your data and ask meaningful questions. Then, you will discover how to answer these question by using your first statistical hypothesis tests: the t-test, the Chi-Square test, the Fisher exact test, and the Pearson correlation test.
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Sample size, Power analysis, and Effect size
In this chapter, you will focus on ways to avoid drawing false conclusions, whether false positives (type I errors) or false negatives (type II errors). Central to avoiding false negatives is understanding the interplay between sample size, power analysis, and effect size.
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Design Considerations in Experimental Design
In this chapter, you will learn how to examine and multiple factors at once, controlling for the effect of confounding variables and examining interactions between variables. You will learn how to use randomization and blocking to build robust tests and how to use the powerful ANOVA method.
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Testing Normality: Parametric and Non-parametric Tests
In this final chapter, you will examine the assumptions underlying statistical tests and learn about how that influences your experimental design. This will include learning whether a variable follows a normal distribution and when you should use non-parametric statistical tests like the Wilcoxon rank-sum test and the Spearman correlation test.
-
1
The Basics of Statistical Hypothesis Testing
FreeIn this chapter, you will learn how to explore your data and ask meaningful questions. Then, you will discover how to answer these question by using your first statistical hypothesis tests: the t-test, the Chi-Square test, the Fisher exact test, and the Pearson correlation test.
-
Design Considerations in Experimental Design
In this chapter, you will learn how to examine and multiple factors at once, controlling for the effect of confounding variables and examining interactions between variables. You will learn how to use randomization and blocking to build robust tests and how to use the powerful ANOVA method.
-
Sample size, Power analysis, and Effect size
In this chapter, you will focus on ways to avoid drawing false conclusions, whether false positives (type I errors) or false negatives (type II errors). Central to avoiding false negatives is understanding the interplay between sample size, power analysis, and effect size.
-
Testing Normality: Parametric and Non-parametric Tests
In this final chapter, you will examine the assumptions underlying statistical tests and learn about how that influences your experimental design. This will include learning whether a variable follows a normal distribution and when you should use non-parametric statistical tests like the Wilcoxon rank-sum test and the Spearman correlation test.
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics @ USAA
Luke Hayden
Postdoctoral Researcher
Luke is a scientist who is interested in how statistics, bioinformatics and machine learning techniques can help to improve our understanding of the natural world. His research projects include using machine learning methods and gene expression data to model biological age and measure tissue rejuvenation and developing statistical methods to measure developmental variability. He has worked in the Averof Lab and Pantalacci/Sémon labs at the ENS de Lyon and holds a PhD from the National University of Ireland, Galway. His personal ambition is to find a really good excuse to use a polar area graph.
See MoreCollaborators
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Chester Ismay
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Amy Peterson