8. Wrapping up Stats Block 1

Author
Affiliation

Johnny van Doorn

University of Amsterdam

Published

September 17, 2025

In this lecture we discuss:

  • Correlation
    • In JASP
  • Linear model
    • In JASP
  • Revisiting NHST
    • Alpha
    • Confidence intervals

Reading: Chapters 1-8.8, not 6

Loose ends

JASP

  • Open non .jasp files (e.g., .sav / .csv) - important for exam
  • Correlation
  • Partial correlation
  • Regression
    • Transforming Adverts (1,000£ vs. 100,000£)
    • Assumptions
    • Outliers
    • Export (not for exam)

Correlation & NHST

We will collect n = 10 observations

  • Set alpha: for which \(t\)’s do we reject \(H_0\)?
    • P(\(t\) | \(H_0: r = 0\))?
    • P(\(t\) | \(H_1: r = 0.3\))?
n <- 10
correlation <- 0.3
t.r <- ( correlation*sqrt(n-2) ) / sqrt(1-correlation^2)
t.r
[1] 0.8894992

Correlation & NHST

We will collect n = 30 observations

  • Set alpha
    • P(\(t\) | \(H_0: r = 0\))?
    • P(\(t\) | \(H_1: r = 0.3\))?
n <- 30
correlation <- 0.3
t.r <- ( correlation*sqrt(n-2) ) / sqrt(1-correlation^2)
t.r
[1] 1.664101

Confidence Intervals

  • Based on the sampling distribution, centered on the observed statistic

Confidence Intervals

  • On repeated sampling, \((100 - \alpha)%\) of the intervals contain population/true value
  • If you conclude the present interval contains true value, you have \(\alpha\)% chance of being wrong (Misconception Mutt 2.1)

Closing

Next Week

  • Exam!
    • Mix of using JASP/interpreting output/conceptual understanding
    • Field book available (like in this weeks WA), including glossary
    • WA will contain old exam questions

Contact

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