It's often hypothesized (and backed in some studies) that smiling can increase leniency, or reduce the effects of wrongdoing among other benefits.
A 1995 study by Marianne LaFrance & Marvin Hecht produced a dataset containing 4 different types of smiles, as well as the judge's leniency against judging wrongdoing when seeing these smiles.
The dataset can be interpreted as follows:
- 1 - false smile
- 2 - is felt smile
- 3 - is miserable smile
- 4 - is neutral control
Leniency: a measure of how lenient the judgments were, higher means the judges were more lenient
Given the above information:
- Plot the leniency by smile type in a parallel box plot
- Based on the box plot above, which smile condition resulted in the highest leniency?
- Is the median leniency for the false smile lower than the 75th percentile leniency score for the neutral expression?
Below is code to import the dataset into a Google Colab or Jupyter notebook to help get you started:
# Here is code to pull the dataset and relevant libraries # into a Google Colab or Jupyter notebook to help get you started import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns df = pd.read_csv('https://raw.githubusercontent.com/erood/interviewqs.com_code_snippets/master/Datasets/smile_leniency.csv') df.head()
Subscribe to premium account to see the solution.Get premium now