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Psychological research methods, experimental design, and analysis
Passed 20 of 23 test cases with the skill, and 18 of 23 without it.
and 1 more in the full report
3 fixed17 passed either way2 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
I have a CSV file `experiment_data.csv` with columns `condition` ('treatment' vs 'control') and `dv`. Write a Python script to check normality with Shapiro-Wilk, test equality of variance with Levene's, run an independent sample t-test, calculate Cohen's d, and compute a Bayes Factor.
| Checked | Without skill | With skill |
|---|---|---|
| Uses pingouin.ttest to perform the independent samples t-test. | Passed | Passed |
| Reports the Cohen's d effect size with 95% confidence intervals from the test output. | Missed | Passed |
| Includes the Bayes Factor (BF10) in the output analysis. | Passed | Passed |
The instructions your model reads when the skill loads.
Psychology is the scientific study of mind and behavior, spanning cognitive processes, social influence, developmental trajectories, clinical disorders, and neuroscience. The field has undergone a methodological revolution since the replication crisis of the 2010s, with new standards for statistical rigor, pre-registration, transparency, and open science fundamentally reshaping how research is conducted and evaluated.
This guide covers the practical aspects of conducting psychology research in the post-replication-crisis era: experimental design with adequate power, pre-registration, appropriate statistical analysis, effect size reporting, and the tools and platforms that support reproducible psychological science. The focus is on what reviewers and editors at top journals now expect.
Whether you are designing a behavioral experiment, analyzing survey data, conducting a psychometric validation, or reviewing a manuscript, these patterns reflect current best practices in the field.
| Design | Advantages | Disadvantages | When to Use |
|---|---|---|---|
| Between-subjects | No carryover effects, simpler | Requires more participants, individual differences | Deception studies, one-shot manipulations |
| Within-subjects | More power, fewer participants | Order effects, demand characteristics | Perception, memory, reaction time |
| Mixed | Combines benefits | Complex analysis | Treatment x individual difference |
from statsmodels.stats.power import TTestIndPower, FTestAnovaPower
import numpy as np
# Two-sample t-test power analysis
analysis = TTestIndPower()
# Question: "How many participants per group for d=0.5, power=0.80?"
n_per_group = analysis.solve_power(
effect_size=0.5, # Cohen's d (medium effect)
alpha=0.05,
power=0.80,
alternative="two-sided",
)
print(f"Required N per group: {int(np.ceil(n_per_group))}") # 64
# For small effects (d=0.2), which are common after replication
n_small = analysis.solve_power(effect_size=0.2, alpha=0.05, power=0.80)
test cases passed, vs 18 / 23 without
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