3.5 - Introduction to Predictive Thinking

Question 1

Why is predictive thinking important in data analysis?

Answer 1

It helps analysts anticipate trends, identify risks, and make proactive, data-driven decisions.

Question 2

What does predictive thinking involve?

Answer 2

Understanding patterns in historical data and reasoning about future possibilities, including uncertainty.

Question 3

Name two examples of predictive questions.

Answer 3

Examples: “What will next month’s sales be?” or “Which customers are likely to churn?”

Question 4

Using past data to make predictions about future outcomes is called ______.

Answer 4

Predictive analysis / forecasting

Question 5

What is a key difference between descriptive and predictive analytics?

Answer 5

Descriptive analytics explains what happened; predictive analytics anticipates what could happen.

Question 6

Why is uncertainty important to consider in predictive thinking?

Answer 6

Because predictions are never guaranteed; understanding uncertainty helps manage risk.

Question 7

Which of these is an example of a predictive use case?
A) Counting last year’s sales
B) Forecasting next quarter’s revenue
C) Listing customer names
D) Creating a pie chart

Answer 7

B) Forecasting next quarter’s revenue

Question 8

Give an example of a simple method to predict trends.

Answer 8

Examples: moving averages, linear projections, trendlines in charts.

Question 9

Patterns or relationships between variables that help forecast outcomes are called ______.

Answer 9

Predictors / features / independent variables

Question 10

Why is exploratory analysis important before predicting?

Answer 10

It helps identify trends, relationships, and anomalies that affect predictive accuracy.

Question 10

Name one field where predictive thinking is widely used.

Answer 10

Examples: finance (stock forecasting), marketing (customer churn), healthcare (risk prediction).

Question 11

How does predictive thinking improve decision-making?

Answer 11

It allows actions to be proactive rather than reactive, increasing efficiency and impact.