W9 Thinking Like a Psychological Scientist

Introduction

  • Presenter: Hannah Keege

  • Topic: Thinking like a psychological scientist and applying skills from IRM in everyday life.

Thinking Like a Psychological Scientist

Importance of Critical Thinking

  • Critical thinking involves not taking information at face value.

  • Key steps:

    • Question the validity, authenticity, and truth of reports.

    • Investigate the sources of knowledge.

    • Avoid reliance on superstition or intuition.

  • Emphasis on logical and rational thought supported by empirical data.

Evidence-Based Decision Making

  • Psychological scientific knowledge is built on critical thinking and empirical evidence.

  • Example - Reading Articles:

    • Analyze claims in articles about the correlation between income and psychological disorders.

    • Article Example: Reports a negative relationship - more psychological diagnoses lead to lower income.

    • Critical Inquiry:

    • Is this a correct conclusion?

    • Correlation does not imply causation; it may be spurious.

    • Explore alternate explanations for the correlation.

Analyzing Personal Anecdotes and Treatments

  • Example: Claims of olive oil curing headaches from a friend.

  • Important considerations:

    • Subjective reports cannot be generalized.

    • Discuss alternative explanations:

    • May be due to massage, placebo effect, or mindfulness rather than the olive oil.

  • Scientific Approach to Treatments:

    • Standard of evidence should involve randomized controlled trials (RCTs).

    • RCTs test the full treatment versus various isolated components.

Scientific Hypothesis and Testing

The Nature of Proof in Science

  • Definition: Science does not prove anything conclusively.

  • Goal: To find evidence for or against a hypothesis; must be able to disprove to support.

  • Falsifiability: A valid hypothesis must be falsifiable.

Logic and Hypothesis Testing

  • Example: Hypothesis - To drive, one must be 18 or over.

    • Use cards in a logic game to test the hypothesis:

    • Cards: 1) Driving a car; 2) 16 years old; 3) Riding a bike; 4) 24 years old.

    • Testing the Rule:

    • To validate the hypothesis, you must look for situations that could falsify it.

    • The correct cards to turn are:

      • Driving a car: Check if under 18.

      • 16 years old: Check if driving.

The Implications of Falsification

  • Finding evidence against a hypothesis doesn't make it false.

  • Always consider confounding factors that may affect results.

Communicating Variability and Uncertainty

Understanding Variability in Data

  • Importance in communicating findings with the public.

  • Issues of Misinterpretation:

    • Illustrates the idea that risk is not destiny (e.g., many smokers do not develop lung cancer).

  • Communication Strategy:

    • Must convey that estimates include uncertainty; singular narratives can be misleading.

Importance of Context in Data Presentation

  • Avoid using terms like "gap," which may imply a lack of variability.

  • Stressing on effect size alongside statistical significance.

Effects of Dichotomization

Avoiding False Dichotomies

  • Most subjects exist within the continuum between categories like rich and poor.

  • Importance of continuous assessment rather than classifying as categorical.

Data Interpretation and Multiple Comparisons

  • Awareness that conducting multiple tests increases the type I error rate.

  • Definition: Type I error - incorrectly rejecting a true null hypothesis.

  • Note on regression to the mean:

    • Extreme values tend to be closer to the mean upon subsequent observations.

Future of Psychological Science and Data Skills

Importance of Collaboration and Continuous Learning

  • Encourage building a network of peers for feedback on research ideas.

  • Essential to read entire research papers, especially methods and results, to understand findings fully.

  • Additional learning opportunities in data courses available.

Responsibility in Data Communication

  • Quote from Nate Silver: "Data does not speak for itself."

  • The individual must synthesize and communicate findings responsibly.

  • Growing job opportunities in psychology combined with data science.

Conclusion

  • Need for statistical literacy in understanding and communicating data in everyday life.

  • Encouragement to pursue further education in statistics and data science.

  • Call to inform others about the ethical use of statistical knowledge for public good.