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Data Colada - Thinking about evidence and vice versa
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Thinking about evidence and vice versa
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Внешние ссылки главной страницы ( 5 ) | |
urisohn.com | .htm |
oid.wharton.upenn.edu/profile/jsimmo/ | .htm |
haas.berkeley.edu/faculty/nelson-leif/ | .htm |
twitter.com/DataColada | @DataColada |
facebook.com/DataColada | Facebook page |
Внутренние ссылки главной страницы ( 96 ) | |
- | |
datacolada.org/about | About |
datacolada.org/feedback_policy | Policy on Soliciting Feedback From Authors |
datacolada.org/toc | Table of Contents |
datacolada.org/78c | [78c] Bayes Factors in Ten Recent Psych Science Papers |
datacolada.org/archives/author/uri | Uri Simonsohn |
datacolada.org/78b | [78b] Hyp-Chart, the Missing Link Between P-values and Bayes Factors |
datacolada.org/78a | [78a] If you think p-values are problematic, wait until you understand Bayes Factors |
datacolada.org/77 | [77] Number-Bunching: A New Tool for Forensic Data Analysis |
datacolada.org/76 | [76] Heterogeneity Is Replicable: Evidence From Maluma, MTurk, and Many Labs |
datacolada.org/archives/author/joeuri | Joe & Uri |
datacolada.org/75 | [75] Intentionally Biased: People Purposely Don't Ignore Information They "Should" Ignore |
datacolada.org/archives/author/bdietvorst | Guest author Berkeley Dietvorst, with Uri |
datacolada.org/74 | [74] In Press at Psychological Science: A New 'Nudge' Supported by Implausible Data |
datacolada.org/archives/author/datacolada_with_frank_yu | Guest co-author: Frank Yu, with Leif and Uri |
datacolada.org/73 | [73] Don't Trust Internal Meta-Analysis |
datacolada.org/archives/author/datacolada_with_joachim | Guest co-author: Joachim Vosgerau and Uri, Leif, & Joe |
datacolada.org/72 | [72] Metacritic Has A (File-Drawer) Problem |
datacolada.org/archives/author/joe | Joe Simmons |
datacolada.org/71 | [71] The (Surprising?) Shape of the File Drawer |
datacolada.org/archives/author/leif | Leif Nelson |
datacolada.org/page/2 | Next |
datacolada.org/page/3 | 3 |
datacolada.org/page/4 | 4 |
datacolada.org/page/8 | 8 |
datacolada.org/70 | [70] How Many Studies Have Not Been Run? Why We Still Think the Average Effect Does Not Exist |
datacolada.org/69 | [69] Eight things I do to make my open research more findable and understandable |
datacolada.org/68 | [68] Pilot-Dropping Backfires (So Daryl Bem Probably Did Not Do It) |
datacolada.org/67 | [67] P-curve Handles Heterogeneity Just Fine |
datacolada.org/66 | [66] Outliers: Evaluating A New P-Curve Of Power Poses |
datacolada.org/65 | [65] Spotlight on Science Journalism: The Health Benefits of Volunteering |
datacolada.org/64 | [64] How To Properly Preregister A Study |
datacolada.org/63 | [63] "Many Labs" Overestimated The Importance of Hidden Moderators |
datacolada.org/62 | [62] Two-lines: The First Valid Test of U-Shaped Relationships |
datacolada.org/61 | [61] Why p-curve excludes ps>.05 |
datacolada.org/60 | [60] Forthcoming in JPSP: A Non-Diagnostic Audit of Psychological Research |
datacolada.org/59 | [59] PET-PEESE Is Not Like Homeopathy |
datacolada.org/58 | [58] The Funnel Plot is Invalid Because of This Crazy Assumption: r(n,d)=0 |
datacolada.org/57 | [57] Interactions in Logit Regressions: Why Positive May Mean Negative |
datacolada.org/56 | [56] TWARKing: Test-Weighting After Results are Known |
datacolada.org/55 | [55] The file-drawer problem is unfixable, and that's OK |
datacolada.org/54 | [54] The 90x75x50 heuristic: Noisy & Wasteful Sample Sizes In The "Social Science Replication Project" |
datacolada.org/53/ | [53] What I Want Our Field To Prioritize |
datacolada.org/52 | [52] Menschplaining: Three Ideas for Civil Criticism |
datacolada.org/51 | [51] Greg vs. Jamal: Why Didn't Bertrand and Mullainathan (2004) Replicate? |
datacolada.org/50 | [50] Teenagers in Bikinis: Interpreting Police-Shooting Data |
datacolada.org/49 | [49] P-Curve Won't Do Your Laundry, But Will Identify Replicable Findings |
datacolada.org/48 | [48] P-hacked Hypotheses Are Deceivingly Robust |
datacolada.org/47 | [47] Evaluating Replications: 40% Full ≠ 60% Empty |
datacolada.org/46 | [46] Controlling the Weather |
datacolada.org/45 | [45] Ambitious P-Hacking and P-Curve 4.0 |
datacolada.org/44 | [44] AsPredicted: Pre-registration Made Easy |
datacolada.org/43 | [43] Rain & Happiness: Why Didn't Schwarz & Clore (1983) 'Replicate' ? |
datacolada.org/42 | [42] Accepting the Null: Where to Draw the Line? |
datacolada.org/41 | [41] Falsely Reassuring: Analyses of ALL p-values |
datacolada.org/40 | [40] Reducing Fraud in Science |
datacolada.org/39 | [39] Power Naps: When do Within-Subject Comparisons Help vs Hurt (yes, hurt) Power? |
datacolada.org/38 | [38] A Better Explanation Of The Endowment Effect |
datacolada.org/37 | [37] Power Posing: Reassessing The Evidence Behind The Most Popular TED Talk |
datacolada.org/36 | [36] How to Study Discrimination (or Anything) With Names; If You Must |
datacolada.org/35 | [35] The Default Bayesian Test is Prejudiced Against Small Effects |
datacolada.org/34 | [34] My Links Will Outlive You |
datacolada.org/33 | [33] "The" Effect Size Does Not Exist |
datacolada.org/32 | [32] Spotify Has Trouble With A Marketing Research Exam |
datacolada.org/31 | [31] Women are taller than men: Misusing Occam's Razor to lobotomize discussions of alternative explanations |
datacolada.org/30 | [30] Trim-and-Fill is Full of It (bias) |
datacolada.org/29 | [29] Help! Someone Thinks I p-hacked |
datacolada.org/28 | [28] Confidence Intervals Don't Change How We Think about Data |
datacolada.org/27 | [27] Thirty-somethings are Shrinking and Other U-Shaped Challenges |
datacolada.org/26 | [26] What If Games Were Shorter? |
datacolada.org/25 | [25] Maybe people actually enjoy being alone with their thoughts |
datacolada.org/24 | [24] P-curve vs. Excessive Significance Test |
datacolada.org/23 | [23] Ceiling Effects and Replications |
datacolada.org/22 | [22] You know what's on our shopping list |
datacolada.org/21 | [21] Fake-Data Colada: Excessive Linearity |
datacolada.org/20 | [20] We cannot afford to study effect size in the lab |
datacolada.org/19 | [19] Fake Data: Mendel vs. Stapel |
datacolada.org/18 | [18] MTurk vs. The Lab: Either Way We Need Big Samples |
datacolada.org/17 | [17] No-way Interactions |
datacolada.org/16 | [16] People Take Baths In Hotel Rooms |
datacolada.org/15 | [15] Citing Prospect Theory |
datacolada.org/14 | [14] How To Win A Football Prediction Contest: Ignore Your Gut |
datacolada.org/13 | [13] Posterior-Hacking |
datacolada.org/12 | [12] Preregistration: Not just for the Empiro-zealots |
datacolada.org/11 | [11] "Exactly": The Most Famous Framing Effect Is Robust To Precise Wording |
datacolada.org/10 | [10] Reviewers are asking for it |
datacolada.org/9 | [9] Titleogy: Some facts about titles |
datacolada.org/8 | [8] Adventures in the Assessment of Animal Speed and Morality |
datacolada.org/7 | [7] Forthcoming in the American Economic Review: A Misdiagnosed Failure-to-Replicate |
datacolada.org/6 | [6] Samples Can't Be Too Large |
datacolada.org/5 | [5] The Consistency of Random Numbers |
datacolada.org/4 | [4] The Folly of Powering Replications Based on Observed Effect Size |
datacolada.org/3 | [3] A New Way To Increase Charitable Donations: Does It Replicate? |
datacolada.org/2 | [2] Using Personal Listening Habits to Identify Personal Music Preferences |
datacolada.org/1 | [1] "Just Posting It" works, leads to new retraction in Psychology |
datacolada.org/78 | Drop That Bayes: A Colada Series on Bayes Factors |
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