Comparing medians

Parametric tests tend to compare means, while non-parametric tests tend to compare medians. This is because if your data is skewed, the mean may not be a good measure of central tendency, and you should work with the median instead.

Each of the university ranking organizations you just looked at also included a "total score" for each university. You'll start by determining if the scores are approximately normal by using a histogram. If they are not, you will perform a Mood's median test to compare their medians. In this exercise you'll work with the CW and ARW university rankings.

Again, while there are multiple ways to create histograms, you'll practice using the .hist() method as before.

A DataFrame with three different these three sets of rankings in rankings_df. The packages pandas as pd, NumPy as np, Matplotlib.pyplot as plt, and the stats package from SciPy have all been loaded for you.

This exercise is part of the course

Foundations of Inference in Python

Hands-on interactive exercise

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

# Plot a histogram of the CW total score
rankings_df['cw_score'].____
plt.show()

Edit and Run Code

This exercise is part of the course

Foundations of Inference in Python

AdvancedSkill Level

4.8+

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In this chapter, we'll explore the relationship between samples and statistically justifiable conclusions. Choosing a sample is the basis of making sound statistical decisions, and we’ll explore how the choice of a sample affects the outcome of your inference.

Exercise 1: Statistical inference and random sampling Exercise 2: Sampling and point estimates Exercise 3: Repeated sampling, point estimates and inference Exercise 4: Sampling and bias Exercise 5: Visualizing samples Exercise 6: Inference and bias Exercise 7: Confidence intervals and sampling Exercise 8: Normal sampling distributions Exercise 9: Calculating confidence intervals Exercise 10: Drawing conclusions from samples

Learn all about applying normality tests, correlation tests, and parametric and non-parametric tests for sound inference. Hypothesis tests are tools, and choosing the right tool for the job is critical for statistical decision-making. While you may be familiar with some of these tests in introductory courses, you'll go deeper to enhance your inferential toolkit in this chapter.

Exercise 1: Normality tests Exercise 2: Testing for normality Exercise 3: Distribution of errors Exercise 4: Fitting a normal distribution Exercise 5: Correlation tests Exercise 6: Testing for correlation Exercise 7: Autocorrelation Exercise 8: Explained variance Exercise 9: Parametric tests Exercise 10: Equal variance Exercise 11: Normality of groups Exercise 12: ANOVA Exercise 13: Non-parametric tests Exercise 14: Comparing rankings Exercise 15: Comparing medians

Current Exercise

In this chapter, you'll measure and interpret effect size in various situations, encounter the multiple comparisons problem, and explore the power of a test in depth. While p-values tell you if a significant effect is present, they don't tell you how strong that effect is. Effect size measures how strong an effect a treatment has. Master the factors underpinning effect size in this chapter.

Exercise 1: Effect size Exercise 2: Effect size for means Exercise 3: Effect size for correlations Exercise 4: Effect size for categorical variables Exercise 5: Multiple comparisons and corrections Exercise 6: Multiple comparisons problem Exercise 7: Bonferonni-Holm correction Exercise 8: Power of a test Exercise 9: What is power anyway?Exercise 10: Power for experimental design Exercise 11: Computing power and sample sizes

You’ll expand your inferential statistics toolkit further with a look at bootstrapping, permutation tests, and methods of combining evidence from p-values. Bootstrapping will provide you with a first look at statistical simulation. In the lesson meta-analysis, you’ll learn all about combining results from multiple studies. You’ll end with a look at permutation tests, a powerful and flexible non-parametric statistical tool.

Exercise 1: Bootstrapping Exercise 2: Bootstrap confidence intervals Exercise 3: Bootstrapping vs. normality Exercise 4: Combining evidence from p-values Exercise 5: Fisher's method in SciPy Exercise 6: Inference using Fisher's method Exercise 7: Summarizing Fisher's method Exercise 8: Permutation tests Exercise 9: Permutation tests for correlations Exercise 10: Permutation tests and bootstrapping Exercise 11: Analyzing skewed data with a permutation test Exercise 12: Course wrap-up video