How Can People Become Happier? A Systematic Review of Preregistered Experiments
Bibliographic record
Abstract
Can happiness be reliably increased? Thousands of studies speak to this question. However, many of them were conducted during a period in which researchers commonly “ p-hacked,” creating uncertainty about how many discoveries might be false positives. To prevent p-hacking, happiness researchers increasingly preregister their studies, committing to analysis plans before analyzing data. We conducted a systematic literature search to identify preregistered experiments testing strategies for increasing happiness. We found surprisingly little support for many widely recommended strategies (e.g., performing random acts of kindness). However, our review suggests that other strategies—such as being more sociable—may reliably promote happiness. We also found strong evidence that governments and organizations can improve happiness by providing underprivileged individuals with financial support. We conclude that happiness research stands on the brink of an exciting new era, in which modern best practices will be applied to develop theoretically grounded strategies that can produce lasting gains in life satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".