Why statistics quantifies uncertainty in SciPy - See It in Action

Why Statistics Quantifies Uncertainty

📖 Scenario: Imagine you are a quality inspector at a factory. You want to check if the machines are producing parts with the right size. But you cannot measure every part because there are too many. So, you take a small sample and use statistics to understand the whole batch.

🎯 Goal: You will create a small dataset of part sizes, calculate the average size, and then use statistics to find the uncertainty in your measurement by calculating the standard error. This shows how statistics helps us understand uncertainty in real life.

📋 What You'll Learn

Create a list of part sizes with exact values

Create a variable for the sample size

Calculate the mean of the part sizes using scipy

Calculate the standard error of the mean using scipy

Print the mean and the standard error

💡 Why This Matters

🌍 Real World

Quality control in factories uses statistics to check if products meet standards without measuring every item.

💼 Career

Data scientists and quality engineers use statistical measures like mean and standard error to make decisions based on sample data.

1

Create the dataset of part sizes

Create a list called part_sizes with these exact values: 10.1, 10.3, 9.9, 10.2, 10.0

SciPy

# Create the list part_sizes with the given values
# Your code here

Need a hint?

Use square brackets to create a list and separate values with commas.

2

Set the sample size variable

Create a variable called sample_size and set it to the length of the part_sizes list using the len() function

SciPy

part_sizes = [10.1, 10.3, 9.9, 10.2, 10.0]
# Create sample_size as the length of part_sizes
# Your code here

Need a hint?

Use len(part_sizes) to get the number of items in the list.

3

Calculate mean and standard error

Import scipy.stats as stats. Then create a variable mean_size that stores the mean of part_sizes using stats.tmean(). Next, create a variable std_error that stores the standard error of the mean using stats.sem() on part_sizes

SciPy

part_sizes = [10.1, 10.3, 9.9, 10.2, 10.0]
sample_size = len(part_sizes)
# Import scipy.stats as stats
# Calculate mean_size using stats.tmean(part_sizes)
# Calculate std_error using stats.sem(part_sizes)
# Your code here

Need a hint?

Use import scipy.stats as stats to import. Then use stats.tmean() and stats.sem() functions.

4

Print the mean and standard error

Write two print statements: one to print mean_size with the text "Mean size:" and another to print std_error with the text "Standard error:"

SciPy

import scipy.stats as stats

part_sizes = [10.1, 10.3, 9.9, 10.2, 10.0]
sample_size = len(part_sizes)

mean_size = stats.tmean(part_sizes)
std_error = stats.sem(part_sizes)
# Print mean_size with label
# Print std_error with label
# Your code here

Need a hint?

Use print("Mean size:", mean_size) and print("Standard error:", std_error).