DataCamp
FreeExperimental Design in Python
4.8(1,842)14k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
Proper experimental design is vital for drawing statistically valid conclusions and measuring treatment effects accurately. This intermediate Python course covers randomized block designs, factorial experiments, power analysis, ANOVA post-hoc evaluations, and nonparametric alternatives for handling complex real-world data.
Who It's For
Data scientists, quantitative researchers, and experimental analysts who want to design rigorous experiments and perform hypothesis testing in Python.
Key Takeaways
- Experimental Setups: Implement randomized block designs and factorial setups to isolate treatment effects accurately.
- Statistical Testing: Conduct t-tests, ANOVA, chi-square tests, and post-hoc pairwise comparisons to identify significant differences.
- Power Analysis & Effect Size: Calculate sample sizes with power analysis and evaluate magnitude using Cohen's d formulations.
- Handling Data Complexities: Address interaction terms, heteroscedasticity, and confounding, or pivot to nonparametric tests when assumptions fail.
About this course
Implement Experimental Design Setups
Learn how to implement the most appropriate experimental design setup for your use case. Learn about how randomized block designs and factorial designs can be implemented to measure treatment effects and draw valid and precise conclusions.Conduct Statistical Analyses on Experimental Data
Deep-dive into performing statistical analyses on experimental data, including selecting and conducting statistical tests, including t-tests, ANOVA tests, and chi-square tests of association. Conduct post-hoc analysis following ANOVA tests to discover precisely which pairwise comparisons are significantly different.Conduct Power Analysis
Learn to measure the effect size to determine the amount by which groups differ, beyond being significantly different. Conduct a power analysis using an assumed effect size to determine the minimum sample size required to obtain a required statistical power. Use Cohen's d formulation to measure the effect size for some sample data, and test whether the effect size assumptions used in the power analysis were accurate.Address Complexities in Experimental Data
Extract insights from complex experimental data and learn best practices for communicating findings to different stakeholders. Address complexities such as interactions, heteroscedasticity, and confounding in experimental data to improve the validity of your conclusions. When data doesn't meet the assumptions of parametric tests, you'll learn to choose and implement an appropriate nonparametric test.Advertisement
