# Data Colada — complete archive in chronological order

Source: https://datacolada.org/
Essays: 100 · Span: 2013-09-17 to 2022-04-08 · Total reading time: ~9 hours

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## Essays

1. [[1] "Just Posting It" works, leads to new retraction in Psychology](https://datacolada.org/1) (2013-09-17) — 4 min
  Uri Simonsohn uncovers a shocking case of fake data in the field of psychology. In this intriguing article, Simonsohn recounts the fortuitous discovery of anomalies in a study involving coin sizes, leading to the retraction of the paper. He highlights implausible correlations among emotion measures and impossibly similar results as key red flags. Simonsohn emphasizes the need for data posting by journals, which would facilitate the identification of invalid data. Dive into this gripping investigation of academic integrity and the quest for truth in scientific research.
2. [[2] Using Personal Listening Habits to Identify Personal Music Preferences](https://datacolada.org/2) (2013-09-26) — 3 min
  Leif Nelson takes a lighthearted approach in this article as he discusses how he uses his personal listening habits to test his students' ability to predict his favorite songs. Through analyzing his iTunes data, Nelson explores the statistical realities and nuances of constructing a good mixtape. From the impact of song duration to the influence of personal preferences, this article offers a witty and insightful perspective on the intersection of music and data analysis. Don't miss out on this entertaining read that may even inspire you to question your own listening habits.
3. [[3] A New Way To Increase Charitable Donations: Does It Replicate?](https://datacolada.org/3) (2013-10-02) — 3 min
  In this intriguing article, Joe Simmons investigates a fascinating phenomenon called the Unit Asking Effect. He delves into a new study that explores how people are more inclined to donate more money to help a group of people if they are first asked how much they would donate to help just one person. Simmons replicates the study and shares his findings, highlighting some interesting differences from the original research. Discover the power of psychology in designing effective interventions and the potential impact it can have on charitable donations.
4. [[4] The Folly of Powering Replications Based on Observed Effect Size](https://datacolada.org/4) (2013-10-14) — 3 min
  Uri Simonsohn challenges the common practice of powering replications based solely on observed effect size. In this thought-provoking essay, he sheds light on the misleading nature of this approach due to publication bias. Simonsohn argues that overestimating effect sizes in original research leads to overestimating the power of subsequent replications. He proposes an alternative approach to evaluating replication results, focusing on tight confidence intervals around zero. Discover why Simonsohn's new perspective on sample size and statistical power brings fresh insights to the field of research.
5. [[5] The Consistency of Random Numbers](https://datacolada.org/5) (2013-10-23) — 3 min
  Leif Nelson delves into the fascinating world of random numbers and explores the surprising patterns and preferences that emerge from our supposedly random choices. By comparing favorite numbers to numbers generated at random, Nelson reveals intriguing correlations and sheds light on our biases when it comes to number selection. From debunking the notion of truly random choices to uncovering the numerical trends in PIN codes, this thought-provoking article challenges our understanding of randomness and offers insight into the human mind's relationship with numbers. Prepare to question everything you thought you knew about randomness!
6. [[6] Samples Can't Be Too Large](https://datacolada.org/6) (2013-11-04) — 2 min
  Joe Simmons challenges the criticism of studies with "overpowered" sample sizes in this thought-provoking essay. He argues that larger sample sizes do not change effect sizes and are beneficial in increasing the precision of results. Simmons emphasizes the problem of underpowered studies in the field and criticizes the nonsensical argument against large samples. As an empirical science, Simmons emphasizes the importance of collecting more data and encourages readers to rethink the methodological norms. Discover why larger sample sizes should never be criticized in this enlightening piece.
7. [[7] Forthcoming in the American Economic Review: A Misdiagnosed Failure-to-Replicate](https://datacolada.org/7) (2013-11-11) — 4 min
  In this highly anticipated forthcoming article in the American Economic Review, Uri, Joe, and Leif delve into a misdiagnosed failure-to-replicate a classic study on anchoring effects. The original study by Ariely, Loewenstein, and Prelec showed that high anchors led to higher payments compared to low anchors. However, the AER replication only focused on one study, and the results were consistent with the original but mistakenly interpreted as a failure. Discover why the replication was imprecise, the insights gained from successful replications, and the implications for anchoring effects. Dive into this thought-provoking analysis and challenge your understanding of replication in economics.
8. [[8] Adventures in the Assessment of Animal Speed and Morality](https://datacolada.org/8) (2013-11-25) — 3 min
  Leif Nelson takes us on an intriguing journey in the realm of animal assessments, exploring the surprising correlations between speed and morality. Delving into the intriguing results of a study that asked participants to rate animals based on their speed and moral worth, Nelson uncovers unexpected insights, including the moral superiority of the Tortoise and the peculiar favoritism towards the Jellyfish. With expert opinions and thought-provoking observations, this article challenges our assumptions and offers a fresh perspective on the intricate connections between humans and the animal kingdom. Prepare to be captivated by this unconventional exploration of speed and morality.
9. [[9] Titleogy: Some facts about titles](https://datacolada.org/9) (2013-12-04) — 3 min
  In this quirky and data-driven essay, Uri Simonsohn delves into the fascinating world of titles and their impact. From clichés to word choices, he unveils intriguing facts about the psychology of naming articles. Discover the most frequently used idioms, the rise of colons, and even the highest scoring Scrabble words found in psychology titles. With a touch of humor and some surprising insights, Simonsohn proves that there's more to a title than meets the eye. Get ready for a delightful exploration of the art of naming in academia.
10. [[10] Reviewers are asking for it](https://datacolada.org/10) (2013-12-09) — 2 min
  Uri, Joe, and Leif discuss the growing trend of requiring authors to disclose flexibility in data collection and analysis in scientific journals. They highlight how these policies align with their own recommendations for increased transparency in research. The authors also propose a standardized disclosure request that reviewers can use when evaluating papers, ultimately aiming to shift community norms towards more rigorous reporting practices. Read this article to gain insight into the ongoing efforts to enhance the integrity and reliability of scientific research.
11. [[11] “Exactly”: The Most Famous Framing Effect Is Robust To Precise Wording](https://datacolada.org/11) (2013-12-19) — 4 min
  Joe and Leif present a fascinating replication study on Tversky & Kahneman's famous framing effect, exploring the role of linguistic artifacts in decision-making. While David Mandel argued that a simple wording change, like adding the word "exactly," could eliminate the effect, the replication findings tell a different story. Contrary to Mandel's hypothesis, the framing effect remained strong even with this linguistic clarification. This thought-provoking analysis challenges our understanding of decision biases and highlights the complexity of human perception. Dive into the article to uncover the intricacies of framing effects and the ongoing debate surrounding them.
12. [[12] Preregistration: Not just for the Empiro-zealots](https://datacolada.org/12) (2014-01-07) — 3 min
  Discover the transformative power of preregistration in scientific research as Leif Nelson shares his personal journey towards becoming an advocate for this practice. Unveiling the benefits of transparency, Nelson explains how preregistration helps distinguish between confirmatory and exploratory measures, ultimately enhancing the validity of research findings. Join Nelson on this experimental ride, as he admits to making mistakes and embraces the imperfect nature of scientific progress. Dive into this thought-provoking narrative to gain valuable insights and explore how preregistration can elevate the credibility and impact of your own research endeavors.
13. [[13] Posterior-Hacking](https://datacolada.org/13) (2014-01-13) — 3 min
  Uri Simonsohn's latest article on "Posterior-Hacking" challenges the popular belief that p-hacking only invalidates p-values and not Bayesian inference. With two compelling examples, Simonsohn demonstrates how selective reporting can undermine both approaches. From absurd results in a rejuvenation experiment to simulations on p-hacking, he reveals the alarming ease with which false findings are obtained. This thought-provoking essay brings transparency to the forefront, emphasizing the crucial need for disclosure in both traditional and Bayesian statistics. Dive into this eye-opening piece to gain insights into the flaws and potential solutions that affect the validity of research.
14. [[14] How To Win A Football Prediction Contest: Ignore Your Gut](https://datacolada.org/14) (2014-02-01) — 4 min
15. [[15] Citing Prospect Theory](https://datacolada.org/15) (2014-02-10) — 3 min
  Uri Simonsohn dives into the unparalleled influence of Kahneman and Tversky's legendary "Prospect Theory" article published in 1979. With over 9,206 citations, it not only holds the record for the most cited article in Econometrica, but also outshines any other economics journal publication. Simonsohn explores the enduring popularity of Prospect Theory, examining the "Nobel bump" effect on citation rates and revealing the top researchers and journals that have referenced it. Discover the fascinating interplay between economics and psychology as Simonsohn sheds light on this influential piece that continues to shape our understanding of decision-making under risk.
16. [[16] People Take Baths In Hotel Rooms](https://datacolada.org/16) (2014-02-26) — 3 min
  Joe Simmons delves into the peculiar topic of bathing in hotel rooms, sparked by a debate he had with a colleague. He shares surprising statistics about the percentage of people who willingly choose to take a bath rather than a shower in their hotel accommodations. Exploring factors such as gender differences, hotel quality, and perceptions of cleanliness, Simmons presents a lighthearted and amusing analysis of this unexpected phenomenon. Prepare to be taken aback by the unexpected insights and perhaps question your own bathing habits while traveling.
17. [[17] No-way Interactions](https://datacolada.org/17) (2014-03-12) — 4 min
  Uri Simonsohn presents an eye-opening fact about interactions in studies that predict smaller effects. Using a famous research paper as an example, he reveals that to maintain the same level of power as in Study 1, Study 2 requires at least twice the number of subjects per cell. Simonsohn discusses the implications of this finding and highlights the common misconceptions and pitfalls that often occur in interpreting interactions. If you want a fresh perspective on the reliability of interaction studies and the importance of statistical power, this thought-provoking article is a must-read.
18. [[18] MTurk vs. The Lab: Either Way We Need Big Samples](https://datacolada.org/18) (2014-04-04) — 4 min
  Joe Simmons delves into the age-old debate of online versus in-person data collection in psychology studies. In his research, he investigates whether Mechanical Turk (MTurk) participants yield different results compared to those in traditional lab settings. Contrary to popular belief, Simmons reveals that the effect sizes obtained from MTurk and lab samples were strikingly similar. He challenges the notion that online studies need significantly larger sample sizes and emphasizes the importance of monitoring data quality regardless of the platform used. Prepare to have your assumptions challenged in this intriguing exploration of sampling methods.
19. [[19] Fake Data: Mendel vs. Stapel](https://datacolada.org/19) (2014-04-14) — 4 min
  Uri Simonsohn delves into the intriguing case of fake data in scientific research, comparing the fraudulent actions of psychologists Diederik Stapel, Dirk Smeesters, and Lawrence Sanna with the controversial findings of Gregor Mendel. While the psychologists' data lacked noise, indicating fabricated results, Mendel's data also lacked sampling error, but with a crucial difference – his motive. Simonsohn explores the impact of motive on data anomalies, proposing that Mendel's motive for lacking noise adds reasonable doubt to accusations of fabrication, while the psychologists' lack of motive solidifies their guilt. Discover the fascinating nuances and motivations behind questionable research practices in this thought-provoking analysis.
20. [[20] We cannot afford to study effect size in the lab](https://datacolada.org/20) (2014-05-01) — 3 min
  Uri Simonsohn challenges the prevailing notion that researchers should prioritize estimating effect sizes over testing for significance, arguing that in the lab, it's simply not feasible. With a median sample size of only 20 in psychology studies, he questions the practicality of reporting effect size accurately. Simonsohn makes a compelling case using Cohen's d as an index and explains why larger sample sizes are needed to truly understand and generalize effect size. If you're interested in the methodology and limitations of effect size estimation, this thought-provoking article is a must-read.
21. [[21] Fake-Data Colada: Excessive Linearity](https://datacolada.org/21) (2014-05-08) — 5 min
22. [[22] You know what's on our shopping list](https://datacolada.org/22) (2014-05-22) — 3 min
23. [[23] Ceiling Effects and Replications](https://datacolada.org/23) (2014-06-04) — 5 min
24. [[24] P-curve vs. Excessive Significance Test](https://datacolada.org/24) (2014-06-27) — 3 min
25. [[25] Maybe people actually enjoy being alone with their thoughts](https://datacolada.org/25) (2014-07-22) — 5 min
26. [[26] What If Games Were Shorter?](https://datacolada.org/26) (2014-08-22) — 5 min
27. [[27] Thirty-somethings are Shrinking and Other U-Shaped Challenges](https://datacolada.org/27) (2014-09-17) — 5 min
28. [[28] Confidence Intervals Don't Change How We Think about Data](https://datacolada.org/28) (2014-10-08) — 3 min
29. [[29] Help! Someone Thinks I p-hacked](https://datacolada.org/29) (2014-10-22) — 3 min
30. [[30] Trim-and-Fill is Full of It (bias)](https://datacolada.org/30) (2014-12-03) — 3 min
31. [[31] Women are taller than men: Misusing Occam’s Razor to lobotomize discussions of alternative explanations](https://datacolada.org/31) (2014-12-18) — 3 min
32. [[32] Spotify Has Trouble With A Marketing Research Exam](https://datacolada.org/32) (2015-01-12) — 4 min
33. [[33] "The" Effect Size Does Not Exist](https://datacolada.org/33) (2015-02-09) — 3 min
34. [[34] My Links Will Outlive You](https://datacolada.org/34) (2015-03-02) — 3 min
35. [[35] The Default Bayesian Test is Prejudiced Against Small Effects](https://datacolada.org/35) (2015-04-09) — 6 min
36. [[36] How to Study Discrimination (or Anything) With Names; If You Must](https://datacolada.org/36) (2015-04-23) — 6 min
37. [[37] Power Posing: Reassessing The Evidence Behind The Most Popular TED Talk](https://datacolada.org/37) (2015-05-08) — 7 min
38. [[38] A Better Explanation Of The Endowment Effect](https://datacolada.org/38) (2015-05-27) — 4 min
39. [[39] Power Naps: When do Within-Subject Comparisons Help vs Hurt (yes, hurt) Power?](https://datacolada.org/39) (2015-06-22) — 6 min
40. [[40] Reducing Fraud in Science](https://datacolada.org/40) (2015-06-29) — 5 min
41. [[41] Falsely Reassuring: Analyses of ALL p-values](https://datacolada.org/41) (2015-08-24) — 4 min
42. [[42] Accepting the Null: Where to Draw the Line?](https://datacolada.org/42) (2015-10-28) — 6 min
43. [[43] Rain & Happiness: Why Didn’t Schwarz & Clore (1983) ‘Replicate’ ?](https://datacolada.org/43) (2015-11-16) — 6 min
44. [[44] AsPredicted:  Pre-registration Made Easy](https://datacolada.org/44) (2015-12-01) — 3 min
45. [[45] Ambitious P-Hacking and P-Curve 4.0](https://datacolada.org/45) (2016-01-14) — 4 min
46. [[46] Controlling the Weather](https://datacolada.org/46) (2016-02-02) — 4 min
47. [[47] Evaluating Replications: 40% Full ≠ 60% Empty](https://datacolada.org/47) (2016-03-03) — 7 min
48. [[48] P-hacked Hypotheses Are Deceivingly Robust](https://datacolada.org/48) (2016-04-28) — 5 min
49. [[49] P-Curve Won’t Do Your Laundry, But Will Identify Replicable Findings](https://datacolada.org/49) (2016-06-14) — 6 min
50. [[50] Teenagers in Bikinis: Interpreting Police-Shooting Data](https://datacolada.org/50) (2016-07-14) — 6 min
51. [[51] Greg vs. Jamal: Why Didn’t Bertrand and Mullainathan (2004) Replicate?](https://datacolada.org/51) (2016-09-06) — 7 min
52. [[52] Menschplaining: Three Ideas for Civil Criticism](https://datacolada.org/52) (2016-09-26) — 5 min
53. [[53] What I Want Our Field To Prioritize](https://datacolada.org/53) (2016-09-30) — 7 min
54. [[54] The 90x75x50 heuristic: Noisy & Wasteful Sample Sizes In The “Social Science Replication Project”](https://datacolada.org/54) (2016-11-01) — 5 min
55. [[55] The file-drawer problem is unfixable, and that’s OK](https://datacolada.org/55) (2016-12-17) — 4 min
56. [[56] TWARKing: Test-Weighting After Results are Known](https://datacolada.org/56) (2017-01-03) — 4 min
57. [[57] Interactions in Logit Regressions: Why Positive May Mean Negative](https://datacolada.org/57) (2017-02-23) — 7 min
58. [[58] The Funnel Plot is Invalid Because of This Crazy Assumption: r(n,d)=0](https://datacolada.org/58) (2017-03-21) — 5 min
59. [[59] PET-PEESE Is Not Like Homeopathy](https://datacolada.org/59) (2017-04-12) — 7 min
60. [[60] Forthcoming in JPSP: A Non-Diagnostic Audit of Psychological Research](https://datacolada.org/60) (2017-05-08) — 8 min
61. [[61] Why p-curve excludes ps>.05](https://datacolada.org/61) (2017-06-15) — 5 min
62. [[62] Two-lines: The First Valid Test of U-Shaped Relationships](https://datacolada.org/62) (2017-09-18) — 7 min
63. [[63] "Many Labs" Overestimated The Importance of Hidden Moderators](https://datacolada.org/63) (2017-10-20) — 10 min
64. [[64] How To Properly Preregister A Study](https://datacolada.org/64) (2017-11-06) — 7 min
65. [[65] Spotlight on Science Journalism: The Health Benefits of Volunteering](https://datacolada.org/65) (2017-11-13) — 7 min
66. [[66] Outliers: Evaluating A New P-Curve Of Power Poses](https://datacolada.org/66) (2017-12-06) — 10 min
67. [[67] P-curve Handles Heterogeneity Just Fine](https://datacolada.org/67) (2018-01-08) — 11 min
68. [[68] Pilot-Dropping Backfires (So Daryl Bem Probably Did Not Do It)](https://datacolada.org/68) (2018-01-25) — 6 min
69. [[69] Eight things I do to make my open research more findable and understandable](https://datacolada.org/69) (2018-02-06) — 4 min
70. [[70] How Many Studies Have Not Been Run? Why We Still Think the Average Effect Does Not Exist](https://datacolada.org/70) (2018-03-09) — 8 min
71. [[71] The (Surprising?) Shape of the File Drawer](https://datacolada.org/71) (2018-04-30) — 7 min
72. [[72] Metacritic Has A (File-Drawer) Problem](https://datacolada.org/72) (2018-07-02) — 7 min
73. [[73] Don't Trust Internal Meta-Analysis](https://datacolada.org/73) (2018-10-24) — 8 min
74. [[74] In Press at Psychological Science: A New 'Nudge' Supported by Implausible Data](https://datacolada.org/74) (2018-12-05) — 9 min
75. [[75] Intentionally Biased: People Purposely Don't Ignore Information They "Should" Ignore](https://datacolada.org/75) (2019-01-29) — 4 min
76. [[76] Heterogeneity Is Replicable: Evidence From Maluma, MTurk, and Many Labs](https://datacolada.org/76) (2019-04-24) — 13 min
77. [[77] Number-Bunching: A New Tool for Forensic Data Analysis](https://datacolada.org/77) (2019-05-25) — 9 min
78. [Drop That Bayes: A Colada Series on Bayes Factors](https://datacolada.org/78) (2019-11-13) — 1 min
79. [[79] Experimentation Aversion: Reconciling the Evidence](https://datacolada.org/79) (2019-11-07) — 9 min
80. [[80] Interaction Effects Need Interaction Controls](https://datacolada.org/80) (2019-11-20) — 5 min
81. [[81] Data Replicada](https://datacolada.org/81) (2019-12-09) — 4 min
82. [[82] Data Replicada #1: Do Elevated Viewpoints Increase Risk Taking?](https://datacolada.org/82) (2019-12-11) — 6 min
83. [[83] Data Replicada #2: Do Self-Construal and Group Size Influence How People Make Choices on Behalf of a Group?](https://datacolada.org/83) (2020-01-15) — 11 min
84. [[84] Data Replicada #3: Does Self-Concept Uncertainty Influence Magazine Subscription Choice?](https://datacolada.org/84) (2020-02-11) — 8 min
85. [[85] Data Replicada #4: The Problem of Hidden Confounds](https://datacolada.org/85) (2020-03-10) — 7 min
86. [[86] The Data Colada Seminar Series](https://datacolada.org/86) (2020-04-20) — 2 min
87. [[87] Data Replicada #5: Do Human-Like Products Inspire More Holistic Judgments?](https://datacolada.org/87) (2020-05-20) — 7 min
88. [[88] The Hot-Hand Artifact for Dummies & Behavioral Scientists](https://datacolada.org/88) (2020-05-27) — 15 min
89. [[89] Data Replicada #6: The Problem of (Weird) Differential Attrition](https://datacolada.org/89) (2020-07-21) — 14 min
90. [[90] Data Replicada #7: Does Displaying Multiple Copies of a Product Increase Its Perceived Effectiveness?](https://datacolada.org/90) (2020-08-18) — 9 min
91. [[91] p-hacking fast and slow: Evaluating a forthcoming AER paper deeming some econ literatures less trustworthy](https://datacolada.org/91) (2020-09-15) — 13 min
92. [[92] Data Replicada #8: Is The Left-Digit Bias Stronger When Prices Are Presented Side-By-Side?](https://datacolada.org/92) (2020-10-01) — 8 min
93. [[93] ResearchBox: Open Research Made Easy](https://datacolada.org/93) (2020-10-30) — 5 min
94. [[94] Data Replicada #9: Are Progression Ads More Credible?](https://datacolada.org/94) (2020-12-03) — 5 min
95. [[95] Groundhog: Addressing The Threat That R Poses To Reproducible Research](https://datacolada.org/95) (2021-01-05) — 8 min
96. [[96] Madam Speaker: Are Female Presenters Treated Worse in Econ Seminars?](https://datacolada.org/96) (2021-04-30) — 6 min
97. [[97] Data Replicada #10: Does Goal Conflict Affect Time Spent on Work and Leisure?](https://datacolada.org/97) (2021-05-04) — 11 min
98. [[98] Evidence of Fraud in an Influential Field Experiment About Dishonesty](https://datacolada.org/98) (2021-08-17) — 17 min
99. [[99] Hyping Fisher: The Most Cited 2019 QJE Paper Relied on an Outdated Stata Default to Conclude Regression p-values Are Inadequate](https://datacolada.org/99) (2021-10-13) — 5 min
100. [[100] Groundhog 2.0: Further addressing the threat R poses to reproducible research](https://datacolada.org/100) (2022-04-08) — 8 min