Estimating required sample size for energy study

In the energy sector, researchers are often tasked with evaluating the effectiveness of new technologies or initiatives to enhance energy efficiency or reduce consumption. A study is being designed to compare the impact of two energy-saving measures: "Smart Thermostats" and "LED Lighting". To ensure the study has sufficient power to detect a meaningful difference in energy savings between these two measures, you'll conduct a power analysis.

pandas as pd, numpy as np, and from statsmodels.stats.power import TTestIndPower are loaded.

This exercise is part of the course

Experimental Design in Python

Exercise instructions

Instantiate a TTestIndPower object.
Conduct the power analysis to estimate the required sample size for each group (Smart Thermostats and LED Lighting) to achieve a power of 0.9, assuming a moderate effect size (Cohen's d = 0.5) and an alpha of 0.05 with an equal sized groups.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Instantiate a TTestIndPower object
power_analysis = ____

# Conduct a power analysis to determine the required sample size
required_n = power_analysis.solve_power(
    effect_size=____, 
    alpha=____, 
    power=____, 
    ratio=____)

print(required_n)

Edit and Run Code

This exercise is part of the course

Experimental Design in Python

IntermediateSkill Level

4.8+

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Building knowledge in experimental design allows you to test hypotheses with best-practice analytical tools and quantify the risk of your work. You’ll begin your journey by setting the foundations of what experimental design is and different experimental design setups such as blocking and stratification. You’ll then learn and apply visual and analytical tests for normality in experimental data.

Exercise 1: Setting up experiments Exercise 2: Non-random assignment of subjects Exercise 3: Random assignment of subjects Exercise 4: Experimental data setup Exercise 5: Blocking experimental data Exercise 6: Stratifying an experiment Exercise 7: Which was stratified?Exercise 8: Normal data Exercise 9: Visual normality in an agricultural experiment Exercise 10: Analytical normality in an agricultural experiment

You'll delve into sophisticated experimental design techniques, focusing on factorial designs, randomized block designs, and covariate adjustments. These methodologies are instrumental in enhancing the accuracy, efficiency, and interpretability of experimental results. Through a combination of theoretical insights and practical applications, you'll acquire the skills needed to design, implement, and analyze complex experiments in various fields of research.

Exercise 1: Factorial designs: principles and applications Exercise 2: Understanding marketing campaign effectiveness Exercise 3: Heatmap of campaign interactions Exercise 4: Factorial designs and randomized block designs Exercise 5: Randomized block design: controlling variance Exercise 6: Implementing a randomized block design Exercise 7: Visualizing productivity within blocks by incentive Exercise 8: ANOVA within blocks of employees Exercise 9: Covariate adjustment in experimental design Exercise 10: Importance of covariates Exercise 11: Covariate adjustment with chick growth

Master statistical tests like t-tests, ANOVA, and Chi-Square, and dive deep into post-hoc analyses and power analysis essentials. Learn to select the right test, interpret p-values and errors, and skillfully conduct power analysis to determine sample and effect sizes, all while leveraging Python's powerful libraries to bring your data insights to life.

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Current Exercise

Hop into the complexities of experimental data analysis. Learn to synthesize insights using pandas, address data issues like heteroscedasticity with scipy.stats, and apply nonparametric tests like Mann-Whitney U. Learn additional techniques for transforming, visualizing, and interpreting complex data, enhancing your ability to conduct robust analyses in various experimental settings.

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