3.2 - Probability & Uncertainty

Question 1

Why is probability important in data analysis?

Answer 1

It helps quantify uncertainty and make informed predictions and decisions.

Question 2

What does “uncertainty” mean in data?

Answer 2

Uncertainty is the lack of complete certainty about outcomes or measurements in data.

Question 3

What is a probability?

Answer 3

A measure of how likely an event is to occur, usually expressed between 0 (impossible) and 1 (certain).

Question 4

An event that is guaranteed to happen has a probability of ______.

Answer 4

1

Question 5

An event that cannot happen has a probability of ______.

Answer 5

0

Question 6

Name one example of probability in everyday data.

Answer 6

Examples: likelihood of rain, customer making a purchase, rolling a 6 on a die.

Question 7

What is the difference between theoretical and empirical probability?

Answer 7

Theoretical is based on reasoning (e.g., coin toss), empirical is based on observed data (e.g., past sales).

Question 8

Probability can help analysts manage ______ when making decisions.

Answer 8

Risk / uncertainty

Question 9

Which of the following is an example of uncertainty in data?
A) Measurement error
B) Random variation
C) Unknown future outcomes
D) All of the above

Answer 9

D) All of the above

Question 10

What is a simple way to visualize probability distributions?

Answer 10

Examples: histograms, probability density plots, bar charts for discrete outcomes.

Question 11

Why do analysts need to consider uncertainty when interpreting results?

Answer 11

Ignoring uncertainty can lead to overconfidence and poor decision-making.

Question 12

Name one method to reduce uncertainty in data analysis.

Answer 12

Examples: increase sample size, improve data quality, use statistical models, gather more information.