The p-Value Debate and Statistical (Mal)practice – Implications for the Agricultural and Food Economics Community

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Cover der Ausgabe: GJAE - German Journal of Agricultural Economics Jahrgang 72 (2023), Heft 01
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GJAE - German Journal of Agricultural Economics

Jahrgang 72 (2023), Heft 01

Autor:innen:
, , , , , , , ,
Verlag
dfv Mediengruppe, Frankfurt am Main
Erscheinungsjahr
2023
ISSN-Online
2191-4028
ISSN-Print
2191-4028

Kapitelinformationen

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Jahrgang 72 (2023), Heft 01

The p-Value Debate and Statistical (Mal)practice – Implications for the Agricultural and Food Economics Community

Autor:innen:
, , ,
ISSN-Print
2191-4028
ISSN-Online
2191-4028
Kapitelvorschau:

Literaturverzeichnis

  1. AAEA (Agricultural & Applied Association) (2021): Call for Papers for a Special Issue on ‘Replications in Agricultural Economics’ in Applied Economic Perspectives and Policy. http://blog.aaea.org/2020/09/call-for-papers-for-special-issue-on.html. Call: 28.10.2021. Google Scholar öffnen
  2. ACZEL, B., R. HOEKSTRA, A. GELMAN, E.-J. WAGENMAKERS, I.G. KLUGKIST, J.N. ROUDER, J. VANDEKERCKHOVE, M.D. LEE, R.D. MOREY, W. VANPAEMEL, Z. DIENES and D. VAN RAVENZWAAIJ (2020): Discussion points for Bayesian inference. In: Nature Human Behaviour 4 (6): 561-563. Google Scholar öffnen
  3. ALBERS, C. (2019): The problem with unadjusted multiple and sequential statistical testing. In: Nature Communications 10 (1): 1921. Google Scholar öffnen
  4. ALTMAN, D.G. and J.M. BLAND (1995): Absence of evidence is not evidence of absence. In: BMJ 311 (7003): 485. Google Scholar öffnen
  5. AMRHEIN, V., S. GREENLAND and B. MCSHANE (2019): Scientists rise up against statistical significance. In: Nature 567 (7748): 305-307. Google Scholar öffnen
  6. AMRHEIN, V., F. KORNER-NIEVERGELT and T. ROTH (2017): The earth is flat (p 0.05): significance thresholds and the crisis of unreplicable research. In: PeerJ 5: e3544. Google Scholar öffnen
  7. ANGRIST, J. and J.-S. PISCHKE (2008): Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press, Princeton. Google Scholar öffnen
  8. ARPINON, T. and R. ESPINOSA (2022): A Practical Guide to Registered Reports for Economists. Working Paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id= Google Scholar öffnen
  9. ASKAROV, Z., A. DOUCOULIAGOS, H. DOUCOULIAGOS and T.D. STANLEY (2022): The Significance of Data-Sharing Policy. In: Journal of the European Economic Association. Google Scholar öffnen
  10. BAKKER, M., C.L.S. VELDKAMP, M.A.L.M. VAN ASSEN, E.A.V. CROMPVOETS, H.H. ONG, B.A. NOSEK, C.K. SODERBERG, D. MELLOR and J.M. WICHERTS (2020): Ensuring the quality and specificity of preregistrations. In: PLOS Biology 18 (12): e3000937. Google Scholar öffnen
  11. BANERJEE, A., E. DUFLO, A. FINKELSTEIN, L. KATZ, B. OLKEN and A. SAUTMANN (2020): In Praise of Moderation: Suggestions for the Scope and Use of Pre-Analysis Plans for RCTs in Economics. Working Paper 26993. National Bureau of Economic Researc. Google Scholar öffnen
  12. BARKASZI, L., S. KESZTHELYI, E. K. CSATÁRI and C. PESTI (2009): FADN Accountancy Framework and Cost Definitions. FACEPA Deliverable No. D1.1.1 - July 2009. In: http://facepa.slu.se/documents/Deliverable_D1-1-1_ Google Scholar öffnen
  13. LEI.pdf. Call: 9.3.2020. Google Scholar öffnen
  14. BARREIRO-HURLÉ, J. (2021): Spanish Journal of Agricultural Research Editorial Policy Update: Pre-registration of submissions based on primary data. In: Spanish Journal of Agricultural Research 19 (4): e01105. Google Scholar öffnen
  15. BASTARDI, A., E. L. UHLMANN and L. ROSS (2011): Wishful thinking: belief, desire, and the motivated evaluation of scientific evidence. In: Psychological science 22 (6): 731-732. Google Scholar öffnen
  16. BEKKERMAN, A. (2015): The role of simulations in econometrics pedagogy. In: Wiley Interdisciplinary Reviews: Computational Statistics 7 (2): 160-165. Google Scholar öffnen
  17. BENDTSEN, M. (2018): A Gentle Introduction to the Comparison Between Null Hypothesis Testing and Bayesian Analysis: Reanalysis of Two Randomized Controlled Trials. In: Journal of Medical Internet Research 20 (10): e10873. Google Scholar öffnen
  18. BENJAMINI, Y. (2016): It's not the p-values' fault. In: The American Statistician, Online Discussion 70: 1-2. Google Scholar öffnen
  19. BINGHAM, E., J.P. CHEN, M. JANKOWIAK, F. OBERMEYER, N. PRADHAN, T. KARALETSOS, R. SINGH, P. SZERLIP, P. HORSFALL and N.D. GOODMAN (2019): Pyro: Deep Universal Probabilistic Programming. In: Journal of Machine Learning Research 20 (1): 973-978. Google Scholar öffnen
  20. BLANCO-PEREZ, C. and A. BRODEUR (2020): Publication Bias and Editorial Statement on Negative Findings. In: The Economic Journal 130 (629): 1226-1247. Google Scholar öffnen
  21. BRODEUR, A., N. COOK and A. HEYES (2020): Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics. In: American Economic Review 110 (11): 3634-3660. Google Scholar öffnen
  22. BRODEUR, A., M. LÉ, M. SANGNIER and Y. ZYLBERBERG (2016): Star Wars: The Empirics Strike Back. In: American Economic Journal: Applied Economics 8 (1): 1-32. Google Scholar öffnen
  23. BRUNS, S.B., I. ASANOV, R. BODE, M. DUNGER, C. FUNK, S.M. HASSAN, J. HAUSCHILDT, D. HEINISCH, K. KEMPA, J. KÖNIG, J. LIPS, M. VERBECK, E. WOLFSCHÜTZ and G. BUENSTORF (2019): Reporting errors and biases in published empirical findings: Evidence from innovation research. In: Research Policy 48 (9): 103796. Google Scholar öffnen
  24. BRUNS, S.B. and M. KALTHAUS (2020): Flexibility in the selection of patent counts: Implications for p-hacking and evidence-based policymaking. In: Research Policy 49 (1): 103877. Google Scholar öffnen
  25. BUCK, S. (2021): Beware performative reproducibility. In: Nature 595 (7866): 151. Google Scholar öffnen
  26. BUTTON, K.S., J.P.A. IOANNIDIS, C. MOKRYSZ, B.A. NOSEK, J. FLINT, E.S.J. ROBINSON and M.R. MUNAFÒ (2013): Power failure: why small sample size undermines the reliability of neuroscience. In: Nature Reviews Neuroscience 14 (5): 365-376. Google Scholar öffnen
  27. CAMERER, C.F., A. DREBER, E. FORSELL, T.-H. HO, J. HUBER, M. JOHANNESSON, M. KIRCHLER, J. ALMENBERG, A. ALTMEJD, T. CHAN, E. HEIKENSTEN, F. HOLZMEISTER, T. IMAI, S. ISAKSSON, G. NAVE, T. PFEIFFER, M. RAZEN and H. WU (2016): Evaluating replicability of laboratory experiments in economics. In: Science 351 (6280): 1433-1436. Google Scholar öffnen
  28. CHRISTENSEN, G., J. FREESE and E. MIGUEL (2019): Transparent and Reproducible Social Science Research. How to Do Open Science. University of California Press, Berkeley, California. Google Scholar öffnen
  29. CHRISTENSEN, G. and E. MIGUEL (2018): Transparency, Reproducibility, and the Credibility of Economics Research. In: Journal of Economic Literature 56 (3): 920-980. Google Scholar öffnen
  30. CLEMENS, M.A. (2017): The meaning of failed replications: A review and proposal. In: Journal of Economic Surveys 31 (1): 326-342. Google Scholar öffnen
  31. COLQUHOUN, D. (2014): An investigation of the false discovery rate and the misinterpretation of p-values. In: Royal Society Open Science 1 (3): 140216. Google Scholar öffnen
  32. DUMOUCHEL, W. and G.J. DUNCAN (1983): Sample Survey Weights in Multiple Regression Analyses of Stratified Samples. In: Journal of the American Statistical Association 78 (383): 535-543. Google Scholar öffnen
  33. ELLIOTT, M.R. and R. VALLIANT (2017): Inference for Nonprobability Samples. In: Statistical Science 32 (2): 249-264. Google Scholar öffnen
  34. FAUL, F., E. ERDFELDER, A.-G. LANG and A. BUCHNER (2007): G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. In: Behavior Research Methods 39 (2): 175-191. Google Scholar öffnen
  35. FERRARO, P. J. and P. SHUKLA (2020): Feature—Is a Replicability Crisis on the Horizon for Environmental and Resource Economics? In: Review of Environmental Economics and Policy 14 (2): 339-351. Google Scholar öffnen
  36. FERRARO, P.J. and P. SHUKLA (2022): Credibility crisis in agricultural economics. In: Applied Economic Perspectives and Policy. Google Scholar öffnen
  37. FISHER, R.A. (1925): Statistical methods for research workers. Oliver and Boyd, Edinburgh. Google Scholar öffnen
  38. FRICKER, R.D., K. BURKE, X. HAN and W.H. WOODALL (2019): Assessing the Statistical Analyses Used in Basic and Applied Social Psychology After Their p -Value Ban. In: The American Statistician 73 (sup1): 374-384. Google Scholar öffnen
  39. GELMAN, A. (2016): The Problems With P-Values are not Just With P-Values. In: The American Statistician, Online Discussion. Google Scholar öffnen
  40. GELMAN, A. and J. CARLIN (2017): Some Natural Solutions to the p -Value Communication Problem—and Why They Won’t Work. In: Journal of the American Statistical Association 112 (519): 899-901. Google Scholar öffnen
  41. GEWEKE, J., G. KOOP and H. VAN DIJK (2011): Introduction. In: : The Oxford Handbook of Bayesian Econometrics: 1-8. Google Scholar öffnen
  42. GIGERENZER, G. (2004): Mindless statistics. In: The Journal of Socio-Economics 33 (5): 587-606. Google Scholar öffnen
  43. GIGERENZER, G. (2018): Statistical Rituals: The Replication Delusion and How We Got There. In: Advances in Methods and Practices in Psychological Science 1 (2): 198-218. Google Scholar öffnen
  44. GIOFRÈ, D., G. CUMMING, L. FRESC, I. BOEDKER and P. TRESSOLDI (2017): The influence of journal submission guidelines on authors' reporting of statistics and use of open research practices. In: PLOS ONE 12 (4): e0175583. Google Scholar öffnen
  45. GOODMAN, S.N. (2001): Of P-Values and Bayes: A Modest Proposal. In: Epidemiology 12 (3): 295. Google Scholar öffnen
  46. GRANT, M.J. and A. BOOTH (2009): A typology of reviews: an analysis of 14 review types and associated methodologies. In: Health information and libraries journal 26 (2): 91-108. Google Scholar öffnen
  47. GREENLAND, S. (2019): Valid P -Values Behave Exactly as They Should: Some Misleading Criticisms of P -Values and Their Resolution With S -Values. In: The American Statistician 73 (sup1): 106-114. Google Scholar öffnen
  48. GREENLAND, S. (2020): The causal foundations of applied probability and statistics. https://arxiv.org/pdf/2011.02677. Google Scholar öffnen
  49. GREENLAND, S., S.J. SENN, K.J. ROTHMAN, J.B. CARLIN, C. POOLE, S.N. GOODMAN and D.G. ALTMAN (2016): Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. In: European Journal of Epidemiology 31 (4): 337-350. Google Scholar öffnen
  50. HARVEY, C. (2017): Presidential Address: The Scientific Outlook in Financial Economics. In: The Journal of Finance 72 (4): 1399-1440. Google Scholar öffnen
  51. HAVEN, T.L. and L. VAN GROOTEL (2019): Preregistering qualitative research. In: Accountability in Research 26 (3): 229-244. Google Scholar öffnen
  52. HECKELEI, T., S. HÜTTEL, M. ODENING and J. ROMMEL (2023): Replication Data for: The p-value debate and statistical (mal)practice - implications for the agricultural and food economics community. GRO.data. Google Scholar öffnen
  53. HIRSCHAUER, N. (2021): The debate on p-values and statistical inference: What are the consequences for our community? Problems and solutions in statistical practice. GEWISOLA 2021 Pre-Conference Workshop. Google Scholar öffnen
  54. HIRSCHAUER, N., S. GRÜNER, O. MUßHOFF and C. BECKER (2021): A Primer on p-Value Thresholds and α-Levels - Two Different Kettles of Fish. In: German Journal of Agricultural Economics 70 (2): 123-133. Google Scholar öffnen
  55. HIRSCHAUER, N., S. GRÜNER, O. MUßHOFF, C. BECKER and A. JANTSCH (2019): Can p-values be meaningfully interpreted without random sampling? In: Statistics Surveys 14: 71-91. Google Scholar öffnen
  56. HIRSCHAUER, N., G. SVEN, O. MUSSHOFF, F. ULRICH, T. INSA and W. PETER (2016): Die Interpretation des p-Wertes - Grundsätzliche Missverständnisse. In: Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik) 236 (5): 557-575. Google Scholar öffnen
  57. HUNTINGTON‐KLEIN, N., A. ARENAS, E. BEAM, M. BERTONI, J.R. BLOEM, P. BURLI, N. CHEN, P. GRIECO, G. EKPE, T. PUGATCH, M. SAAVEDRA and Y. STOPNITZKY (2021): The influence of hidden researcher decisions in applied microeconomics. In: Economic Inquiry 59 (3): 944-960. Google Scholar öffnen
  58. IMBENS, G.W. (2021): Statistical Significance, p -Values, and the Reporting of Uncertainty. In: Journal of Economic Perspectives 35 (3): 157-174. Google Scholar öffnen
  59. IOANNIDIS, J.P.A., T.D. STANLEY and H. DOUCOULIAGOS (2017): The Power of Bias in Economics Research. In: The Economic Journal 127 (605): F236-F265. Google Scholar öffnen
  60. IONIDES, E.L., A. GIESSING, Y. RITOV and S.E. PAGE (2017): Response to the ASA’s Statement on p -Values: Context, Process, and Purpose. In: The American Statistician 71 (1): 88-89. Google Scholar öffnen
  61. KANG, H. (2021): Sample size determination and power analysis using the G*Power software. In: Journal of Educational Evaluation for Health Professions 18. Google Scholar öffnen
  62. KRANZ, S. and P. PÜTZ (2022): Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics: Comment. In: American Economic Review 112 (9): 3124-3136. Google Scholar öffnen
  63. KRUEGER, J.I. and P.R. HECK (2019): Putting the P -Value in its Place. In: The American Statistician 73 (sup1): 122-128. Google Scholar öffnen
  64. LEMKEN, D. (2021): The price penalty for red meat substitutes in popular dishes and the diversity in substitution. In: PLOS ONE 16 (6): e0252675. Google Scholar öffnen
  65. LOGG, J.M. and C.A. DORISON (2021): Pre-registration: Weighing costs and benefits for researchers. In: Organizational Behavior and Human Decision Processes 167: 18-27. Google Scholar öffnen
  66. LOKEN, E. and A. GELMAN (2017): Measurement error and the replication crisis. In: Science 355 (6325): 584-585. Google Scholar öffnen
  67. MARGARIAN, A. (2022): Beyond P-Value-Obsession: When are Statistical Hypothesis Tests Required and Appropriate? In: German Journal of Agricultural Economics 71 (4): 213-226. Google Scholar öffnen
  68. MCCLOSKEY, D.N. and S.T. ZILIAK (1996): The Standard Error of Regressions. In: Journal of Economic Literature 34 (1): 97-114. Google Scholar öffnen
  69. MERVIS, J. (2014): Research Transparency. Why null results rarely see the light of day. In: Science 345 (6200): 992. Google Scholar öffnen
  70. NEUENFELDT, S. and A. GOCHT (2014): Integrating Econometric and Mathematical Programming Models into an Amendable A Handbook on the use of FADN Database in Programming Models. Thünen Working Paper No. 35. https://literatur.thuenen.de/digbib_extern/dn054328.pdf. Google Scholar öffnen
  71. NEYMAN, J. and E.S. PEARSON (1933): On the problem of the most efficient tests of statistical hypotheses. In: Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 231 (694-706): 289-337. Google Scholar öffnen
  72. O’BOYLE, E.H., G.C. BANKS and E. GONZALEZ-MULÉ (2017): The Chrysalis Effect. In: Journal of Management 43 (2): 376-399. Google Scholar öffnen
  73. OAKS, M. (1986): Statistical inference: A commentary for the social and behavioral sciences. Wiley, New York. Google Scholar öffnen
  74. OLKEN, B.A. (2015): Promises and Perils of Pre-Analysis Plans. In: Journal of Economic Perspectives 29 (3): 61-80. Google Scholar öffnen
  75. PÜTZ, P. and S.B. BRUNS (2021): The (Non‐)Significance of Reporting Errors In Economics: Evidence from Three Top Journals. In: Journal of Economic Surveys 35 (1): 348-373. Google Scholar öffnen
  76. RAHWAN, Z., E. YOELI and B. FASOLO (2019): Heterogeneity in banker culture and its influence on dishonesty. In: Nature 575 (7782): 345-349. Google Scholar öffnen
  77. ROMANO, J.P., A.M. SHAIKH and M. WOLF (2010): Multiple Testing. In: The New Palgrave Dictionary of Economics 4. Google Scholar öffnen
  78. ROMMEL, J. and M. WELTIN (2021): Is There a Cult of Statistical Significance in Agricultural Economics? In: Applied Economic Perspectives and Policy 43 (3): 1176-1191. Google Scholar öffnen
  79. SCHOOLER, J.W. (2014): Metascience could rescue the 'replication crisis'. In: Nature 515 (7525): 9. Google Scholar öffnen
  80. SERRA-GARCIA, M. and U. GNEEZY (2021): Nonreplicable publications are cited more than replicable ones. In: Science Advances 7 (21). Google Scholar öffnen
  81. SMITH, T.M.F. (1983): On the Validity of Inferences from Non-random Sample. In: Journal of the Royal Statistical Society. Series A (General) 146 (4): 394. Google Scholar öffnen
  82. STEEGEN, S., F. TUERLINCKX, A. GELMAN and W. VANPAEMEL (2016): Increasing Transparency Through a Multiverse Analysis. In: Perspectives on psychological science : a journal of the Association for Psychological Science 11 (5): 702-712. Google Scholar öffnen
  83. VAN DE MEENT, J.-W., B. PAIGE, H. YANG and F. WOOD (2018): An Introduction to Probabilistic Programming. In: https://arxiv.org/pdf/1809.10756. Google Scholar öffnen
  84. VERHULST, B. (2016): In Defense of P Values. In: AANA journal 84 (5): 305-308. Google Scholar öffnen
  85. WASSERSTEIN, R.L. and N.A. LAZAR (2016): The ASA Statement on p -Values: Context, Process, and Purpose. In: The American Statistician 70 (2): 129-133. Google Scholar öffnen
  86. WASSERSTEIN, R.L., A.L. SCHIRM and N.A. LAZAR (2019): Moving to a World Beyond “ p < 0.05”. In: The American Statistician 73 (sup1): 1-19. Google Scholar öffnen
  87. WEHRDEN, H. von, J. SCHULTNER and D.J. ABSON (2015): A call for statistical editors in ecology. In: Trends in Ecology and Evolution 30 (6): 293-294. Google Scholar öffnen
  88. YOUNG, C. and K. HOLSTEEN (2017): Model Uncertainty and Robustness. In: Sociological Methods & Research 46 (1): 3-40. Google Scholar öffnen
  89. ZILIAK, S. and D. MCCLOSKEY (2008): The Cult of Statistical Significance. How the Standard Error Costs Us Jobs, Justice, and Lives. University of Michigan Press, Ann Arbor, MI. Google Scholar öffnen
  90. ZILIAK, S.T. and D.N. MCCLOSKEY (2004): Size matters: the standard error of regressions in the American Economic Review. In: The Journal of Socio-Economics 33 (5): 527-546. Google Scholar öffnen

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