Exploring and understanding data

PSYC 11: Laboratory in Psychological Science

Jeremy R. Manning
Dartmouth College
Spring 2026

Truth and data

  • The "universe" produces data
  • Math doesn't lie -- but analyses involve choices
  • Different analyses can lead to different conclusions
  • The same dataset can tell very different stories

What patterns should you look for?

  • Shape: How many observations? How many features? Any missing data?
  • Distributions: Are values clustered? Spread out? Skewed?
  • Relationships: Do any features move together? In opposite directions?
  • Outliers: Are there values that seem "wrong" or surprising?

The power of visualization

  • Tables of numbers hide patterns; plots reveal them
  • Different plot types answer different questions:
    • Histograms: What does the distribution look like?
    • Scatter plots: How are two variables related?
    • Bar charts: How do groups compare?
  • Always look at your data before running statistics

Discussion: "What does this graph tell you?"

  • Each group: create a quick plot from your sleuthing dataset
  • Trade plots with a neighboring group
  • For the plot you receive, discuss:
    • What story does this plot tell?
    • What is it not telling you?
    • What follow-up plot would you want to see next?

Analytic flexibility

  • There are typically many ways to analyze data
  • Different choices (which subset, which test, which visualization) can lead to different conclusions
  • This is why transparency about your analysis choices matters

What's in your toolkit?

  • Observation, intuition, and logic
  • Simple summaries (mean, standard deviation, sorting)
  • Traditional statistical tests (t-tests, correlations, ANOVAs)
  • Fancier methods and simulations

Getting help

  • Teaching staff (instructor + TAs)
  • Other students
  • Slack (#stats-stuff, #data-sleuthing-lab)
  • Google, Stack Exchange, Wikipedia, ChatGPT/Claude/ai.dartmouth.edu