M5.0 Why Inferences Matter

Case Study: “The Mystery of the Missing Points”

Background:
Jamie is a first-year student taking Intro to Psychology. Last week, his professor returned the graded essays. Jamie was surprised to see a C+ on his paper, even though the professor hadn’t written very many comments. At the end of the feedback, it simply said: “Work on connecting your examples more clearly to your main argument.”

Jamie is frustrated. He thinks, “She didn’t say my writing was wrong, and I followed the directions. How was I supposed to know what she wanted?”

The next class, a friend shows Jamie their essay with an A grade. The professor wrote: “Excellent use of case studies to reinforce your argument—you consistently tied your examples back to the thesis.”

The Inference Challenge

To figure out what the professor wants, Jamie can’t just rely on what is explicitly written. He needs to make an inference:

• What is implied by the feedback?

• How does comparing the two sets of comments help Jamie draw a conclusion?

• What can Jamie reasonably guess about how to improve his next essay?

Guiding Questions for Students

1. What is the professor implying by writing, “Work on connecting your examples more clearly to your main argument”?

2. How can Jamie use the feedback on his friend’s paper to fill in what his professor values?

3. If Jamie doesn’t practice making inferences, what might he miss about how to succeed in this class?

4. In the real world, where else might you have to read “between the lines” (e.g., job postings, emails, conversations with supervisors)?

Why This Matters

• Professors, bosses, and even friends don’t always spell everything out.

• Success often depends on being able to notice clues and put them together into a bigger picture.

• Inference isn’t guessing—it’s making a logical conclusion based on evidence and context.

Case Study: “The Caffeine Question”

Background:
In her health class, Emily’s professor shows a simple bar graph of average caffeine consumption (in milligrams) per day by age group:

Age Group Average Daily Caffeine (mg)

12–18              60 mg

19–30              165 mg

31–50              210 mg

51+                   180 mg

The graph does not explain why these numbers differ. It just shows the data.

Some students immediately say, “Okay, people in their 30s–50s drink the most caffeine. End of story.”
But Emily realizes she has to make some inferences to really understand what the data might suggest.

The Inference Challenge

Looking at the chart, Emily asks herself:

• Why do 19–30 year olds drink more caffeine than teens? 

• Why does caffeine peak at ages 31–50?

• Why does it drop slightly after 50?

The chart doesn’t directly answer any of this, but the reader can connect real-world knowledge with the data to make an inference.

Guiding Questions for Students

1. What does the chart directly tell you (the facts)?

2. What information is not given but can be reasonably inferred?

3. Why might caffeine use peak in midlife rather than early adulthood?

4. How could jumping to conclusions without using logic lead to misinformation?

Why This Matters

• Charts and graphs give data, but they rarely explain the “why.”

• In both college and careers, you’ll need to interpret numbers, trends, and visuals—and inference is the skill that bridges data with meaning.

• Employers and professors value people who can read between the lines of data, not just repeat what’s already obvious.