Beyond P-Value-Obsession: When are Statistical Hypothesis Tests Required and Appropriate?
Inhaltsverzeichnis
Bibliographische Infos

Kein Zugriff
GJAE - German Journal of Agricultural Economics
Jahrgang 71 (2022), Heft 04
- Autor:innen:
- , , , , , , , ,
- Verlag
- dfv Mediengruppe, Frankfurt am Main
- Erscheinungsjahr
- 2022
- ISSN-Online
- 2191-4028
- ISSN-Print
- 2191-4028
Kapitelinformationen
Kein Zugriff
Jahrgang 71 (2022), Heft 04
Beyond P-Value-Obsession: When are Statistical Hypothesis Tests Required and Appropriate?
- Autor:innen:
- ISSN-Print
- 2191-4028
- ISSN-Online
- 2191-4028
- Kapitelvorschau:
Literaturverzeichnis
Es wurden keine Treffer gefunden. Versuchen Sie einen anderen Begriff.
- ABADIE, A. (2020): Statistical Nonsignificance in Empirical Economics. In: American Economic Review: Insights 2 (2): 193-208. Google Scholar öffnen
- ACKERMAN, B., I. SCHMID, K.E. RUDOLPH, M.J. SEAMANS, R. SUSUKIDA, R. MOJTABAI and E. A. STUART (2019): Implementing statistical methods for generalizing ran-domized trial findings to a target population. In: Addic-tive behaviors 94: 124-132. Google Scholar öffnen
- AMRHEIN, V., D. TRAFIMOW and S. GREENLAND (2019): Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication. In: The American Statistician 73 (sup1): 262-270. Google Scholar öffnen
- BANDYOPADHYAY, P.S. and M.R. FORSTER (2011): Philos-ophy of Statistics. In: Philosophy of Statistics. Elsevier: 1-50. Google Scholar öffnen
- BERK, R., L. BROWN and L. ZHAO (2010): Statistical Infer-ence After Model Selection. In: Journal of Quantitative Criminology 26 (2): 217-236. Google Scholar öffnen
- BERNER, D. and V. AMRHEIN (2022): Why and how we should join the shift from significance testing to estima-tion. In: Journal of evolutionary biology 35 (6): 777-787. Google Scholar öffnen
- BIAU, D.J. (2011): In brief: Standard deviation and stand-ard error. In: Clinical orthopaedics and related research 469 (9): 2661-2664. Google Scholar öffnen
- BLACKWELL, M. (2013): Observational Studies and Con-founding. PSC 504. https://www.mattblackwell.org/teaching/psc504/. Google Scholar öffnen
- BRACHT, G.H. and G.V. GLASS (1968): The External Valid-ity of Experiments. In: American Educational Research Journal 5 (4): 437. Google Scholar öffnen
- BRAND, J.E. and J.S. THOMAS (2013): Causal Effect Het-erogeneity. In: Morgan, S.L. (ed.): Handbook of Causal Analysis for Social Research. Handbooks of Sociology and Social Research. Springer Netherlands, Dordrecht: 189-213. Google Scholar öffnen
- CLARKE, K.A. (2005): The Phantom Menace: Omitted Variable Bias in Econometric Research. In: Conflict Management and Peace Science 22 (4): 341-352. Google Scholar öffnen
- COHEN, H.W. (2011): P values: use and misuse in medical literature. In: American journal of hypertension 24 (1): 18-23. Google Scholar öffnen
- COHEN, J. (1994): The earth is round (p < .05). In: Ameri-can Psychologist 49 (12): 997-1003. Google Scholar öffnen
- COLANDER, D. (2019): Introduction to symposium on teaching undergraduate econometrics. In: The Journal of Economic Education 50 (4): 337-342. Google Scholar öffnen
- COX, D. and D.G. MAYO (2010): Objectivity and Condi-tionality in Frequentist Inference. In: Mayo, D.G. and A. Spanos (eds.): Error and Inference: Recent Exchang-es on Experimental Reasoning, Reliability, and the Ob-jectivity and Rationality of Science. Cambridge Univer-sity Press, Cambridge: 276. Google Scholar öffnen
- DEATON, A. and N. CARTWRIGHT (2018): Understanding and misunderstanding randomized controlled trials. In: Social science & medicine 210 (8): 2-21. Google Scholar öffnen
- FINDLEY, M.G., K. KIKUTA and M. DENLY (2021): Exter-nal Validity. In: Annual Review of Political Science 24 (1): 365-393. Google Scholar öffnen
- FISHER, R.A. (1992): Statistical Methods for Research Workers. In: Kotz, S. and N. L. Johnson (eds.): Break-throughs in Statistics. Springer Series in Statistics. Springer New York, New York, NY: 66-70. Google Scholar öffnen
- GELMAN, A. (2016): The Problems With p-Values Are Not Just With p-Values. supplemental material to the ASA statement on p-values and statistical significance. In: The American Statistician 70 (10). Google Scholar öffnen
- GIGERENZER, G. (2004): Mindless statistics. In: The Journal of Socio-Economics 33 (5): 587-606. Google Scholar öffnen
- GOODMAN, R. and L. KISH (1950): Controlled Selection—A Technique in Probability Sampling. In: Journal of the American Statistical Association 45 (251): 350-372. Google Scholar öffnen
- GOODMAN, S. (2008): A dirty dozen: twelve p-value mis-conceptions. In: Seminars in hematology 45 (3): 135-140. Google Scholar öffnen
- GREENLAND, S. (1990): Randomization, Statistics, and Causal Inference. In: Epidemiology 1 (6): 421-429. Google Scholar öffnen
- GREENLAND, S., S.J. SENN, K.J. ROTHMAN, J.B. CARLIN, C. POOLE, S.N. GOODMAN and D.G. ALTMAN (2016): Sta-tistical tests, P values, confidence intervals, and power: a guide to misinterpretations. In: European journal of epidemiology 31 (4): 337-350. Google Scholar öffnen
- GRIER, K. (2022): Causal Inference and Austrian Econom-ics. In: D'Amico, D.J. and A.G. Martin (eds.): Contem-porary Methods and Austrian Economics. Advances in Austrian Economics. Emerald Publishing Limited, Bingley, UK: 105-114. Google Scholar öffnen
- HAGEN, R.L. (1997): In praise of the null hypothesis statis-tical test. In: American Psychologist 52 (1): 15-24. Google Scholar öffnen
- HAGER, W. (2013): The statistical theories of Fisher and of Neyman and Pearson: A methodological perspective. In: Theory & Psychology 23 (2): 251-270. Google Scholar öffnen
- HALSEY, L.G., D. CURRAN-EVERETT, S.L. VOWLER and G.B. DRUMMOND (2015): The fickle P value generates irreproducible results. In: Nature methods 12 (3): 179-185. Google Scholar öffnen
- HECKELEI, T., S. HÜTTEL, J. ROMMEL and M. ODENING (2022): The Replicability Crisis and the p-Value Debate - what Are the Consequences for the Agricultural and Food Economics Community? Preprints. https://www.preprints.org/manuscript/202201.0311/v1, call: 28.4.2022. Google Scholar öffnen
- HECKMAN, J.J. (1979): Sample Selection Bias as a Specifi-cation Error. In: Econometrica 47 (1): 153. Google Scholar öffnen
- HECKMAN, J.J. (2005): 1. The Scientific Model of Causali-ty. In: Sociological Methodology 35 (1): 1-97. Google Scholar öffnen
- HERRERA-BENNETT, A. (2019): How do researchers evalu-ate statistical evidence when drawing inferences from data? Dissertation zum Erwerb des Doctor of Philoso-phy (Ph.D.). Munich Center of the Learning Sciences, Ludwig-Maximilians-Universitӓt München, Munich. Google Scholar öffnen
- HIRSCHAUER, N. (2022): Unanswered questions in the p ‐value debate. In: Significance 19 (3): 42-44. Google Scholar öffnen
- HIRSCHAUER, N., S. GRÜNER and O. MUßHOFF (2022): Fundamentals of statistical inference. What is the meaning of random error? Springer Nature, Cham. Google Scholar öffnen
- HIRSCHAUER, N., S. GRÜNER, O. MUßHOFF and C. BECKER (2021a): 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
- HIRSCHAUER, N., S. GRÜNER, O. MUßHOFF, C. BECKER and A. JANTSCH (2021b): Inference using non‐random samples? Stop right there! In: Significance 18 (5): 20-24. Google Scholar öffnen
- HIRSCHAUER, N., S. GRÜNER, O. MUßHOFF and C. BECKER (2020a): Inference in economic experiments. In: Eco-nomics 14 (1). Google Scholar öffnen
- HIRSCHAUER, N., S. GRÜNER, O. MUßHOFF, C. BECKER and A. JANTSCH (2020b): Can p-values be meaningfully in-terpreted without random sampling? In: Statistics Sur-veys 14. Google Scholar öffnen
- HIRSCHAUER, N., S. GRÜNER, O. MUßHOFF and C. BECKER (2018): Pitfalls of significance testing and p-value vari-ability: An econometrics perspective. In: Statistics Sur-veys 12. Google Scholar öffnen
- HONG, G. and S.W. RAUDENBUSH (2013): Heterogeneous Agents, Social Interactions, and Causal Inference. In: Morgan, S.L. (ed.): Handbook of Causal Analysis for Social Research. Handbooks of Sociology and Social Research. Springer Netherlands, Dordrecht: 331-352. Google Scholar öffnen
- HUBBARD, R., B.D. HAIG and R.A. PARSA (2019): The Limited Role of Formal Statistical Inference in Scien-tific Inference. In: The American Statistician 73 (sup1): 91-98. Google Scholar öffnen
- IMBENS, G.W. (2021): Statistical Significance, p -Values, and the Reporting of Uncertainty. In: Journal of Eco-nomic Perspectives 35 (3): 157-174. Google Scholar öffnen
- KENNEDY-SHAFFER, L. (2019): Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p-Values and Significance Testing. In: The American Statistician 73 (Suppl 1): 82-90. Google Scholar öffnen
- KUANG, K., L. LI, Z. GENG, L. XU, K. ZHANG, B. LIAO, H. HUANG, P. DING, W. MIAO and Z. JIANG (2020): Caus-al Inference. In: Engineering 6 (3): 253-263. Google Scholar öffnen
- KUHN, T.S. (2009): The structure of scientific revolutions. University of Chicago Press, Chicago. Google Scholar öffnen
- LAKENS, D. (2022): Sample Size Justification. In: Collabra: Psychology 8 (1). Google Scholar öffnen
- LIN, M., H.C. LUCAS and G. SHMUELI (2013): Too Big to Fail: Large Samples and the p-Value Problem. In: In-formation Systems Research 24 (4): 906-917. Google Scholar öffnen
- LUCA, G. de, J.R. MAGNUS and F. PERACCHI (2015): On the Ambiguous Consequences of Omitting Variables. In: SSRN Electronic Journal. Google Scholar öffnen
- LUDWIG, D.A. (2005): Use and Misuse of p-Values in De-signed and Observational Studies. Guide for Research-ers and Reviewers. In: Aviation, Space, and Environ-mental Medicine 76 (7): 675-680. Google Scholar öffnen
- MARKS-ANGLIN, A. and Y. CHEN (2020): A historical re-view of publication bias. In: Research synthesis meth-ods 11 (6): 725-742. Google Scholar öffnen
- MILNER, S. (2018): Newton didn’t frame hypotheses. Why should we? In: Physics Today 24. April 2018. DOI: 10.1063/PT.6.3.20180424a. Google Scholar öffnen
- MURPHY, E.A., E.M. ROSELL and M.I. ROSELL (1986): Deduction, inference and illation. In: Theoretical medi-cine 7 (3): 329-353. Google Scholar öffnen
- NEYMAN, J. and E.S. PEARSON (1928): On the Use and Interpretation of Certain Test Criteria for Purposes of Statistical Inference: Part I. In: Biometrika 20A (1/2): 175. Google Scholar öffnen
- PEARL, J. (2009): Causal inference in statistics: An over-view. In: Statistics Surveys 3: 96-146. Google Scholar öffnen
- PEARL, J. (2010): An introduction to causal inference. In: The international journal of biostatistics 6 (2): Article 7. Google Scholar öffnen
- PETERS, J., J. LANGBEIN and G. ROBERTS (2018): Generali-zation in the Tropics - Development Policy, Random-ized Controlled Trials, and External Validity. In: The World Bank Research Observer 33 (1): 34-64. Google Scholar öffnen
- POIRIER, D.J. (1988): Frequentist and Subjectivist Perspec-tives on the Problems of Model Building in Economics. In: Journal of Economic Perspectives 2 (1): 121-144. Google Scholar öffnen
- RUBIN, D.B. (2008): For objective causal inference, design trumps analysis. In: The Annals of Applied Statistics 2 (3). Google Scholar öffnen
- SCHNEIDER, J.W. (2015): Null hypothesis significance tests. A mix-up of two different theories: the basis for wide-spread confusion and numerous misinterpretations. In: Scientometrics 102 (1): 411-432. Google Scholar öffnen
- SEDDON, P.B. and R. SCHEEPERS (2012): Towards the improved treatment of generalization of knowledge claims in IS research: drawing general conclusions from samples. In: European Journal of Information Systems 21 (1): 6-21. Google Scholar öffnen
- SERDAR, C.C., M. CIHAN, D. YÜCEL and M.A. SERDAR (2021): Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clini-cal and laboratory studies. In: Biochemia medica 31 (1): 10502. Google Scholar öffnen
- SIGNORINO, C.S. and K. YILMAZ (2003): Strategic Misspec-ification in Regression Models. In: American Journal of Political Science 47 (3): 551-566. Google Scholar öffnen
- SPANOS, A. (2000): Probability theory and statistical infer-ence. Econometric modelling with observational data. Cambridge University Press, Cambridge. Google Scholar öffnen
- SPANOS, A. and A. MCGUIRK (2001): The Model Specifica-tion Problem from a Probabilistic Reduction Perspec-tive. In: American Journal of Agricultural Economics 83 (5): 1168-1176. Google Scholar öffnen
- SULLIVAN, G.M. and R. FEINN (2012): Using Effect Size-or Why the P Value Is Not Enough. In: Journal of gradu-ate medical education 4 (3): 279-282. Google Scholar öffnen
- SUMMERFIELD, M.A. (1983): Populations, Samples and Statistical Inference in Geography. In: The Professional Geographer 35 (2): 143-149. Google Scholar öffnen
- THACKER, L.R. (2020): What Is the Big Deal About Popu-lations in Research? In: Progress in transplantation (Ali-so Viejo, Calif.) 30 (1): 3. Google Scholar öffnen
- THOMAS, R.L., P.R. BARACH, J.D. WILKINSON, A.A. FAROOQI and S.E. LIPSHULTZ (2017): The danger of re-lying on the interpretation of p-values in single studies: Irreproducibility of results from clinical studies. In: Pro-gress in Pediatric Cardiology 44 (3): 57-61. Google Scholar öffnen
- TONG, C. (2019): Statistical Inference Enables Bad Sci-ence; Statistical Thinking Enables Good Science. In: The American Statistician 73 (sup1): 246-261. Google Scholar öffnen
- VELLA, F. (1998): Estimating Models with Sample Selec-tion Bias: A Survey. In: The Journal of Human Re-sources 33 (1): 127. Google Scholar öffnen
- WAGENMAKERS, E.-J., M. LEE, T. LODEWYCKX and G.J. IVERSON (2008): Bayesian Versus Frequentist Infer-ence. In: Hoijtink, H., I. Klugkist and P.A. Boelen (eds.): Bayesian Evaluation of Informative Hypotheses. Springer New York, New York, NY: 181-207. Google Scholar öffnen
- WANG, B., Z. ZHOU, H. WANG, X.M. TU and C. FENG (2019): The p-value and model specification in statis-tics. In: General psychiatry 32 (3): e100081. Google Scholar öffnen
- WARD, M.D., B.D. GREENHILL and K.M. BAKKE (2010): The perils of policy by p-value: Predicting civil conflicts. In: Journal of Peace Research 47 (4): 363-375. Google Scholar öffnen
- WASSERSTEIN, R.L. and N.A. LAZAR (2016): The ASA's Statement on p-Values: Context, Process, and Purpose. In: The American Statistician 70 (2): 129-133. Google Scholar öffnen
- WASSERSTEIN, R.L., A.L. SCHIRM and N.A. LAZAR (2019a): Moving to a World Beyond "p < 0.05". In: The American Statistician 73 (sup1): 1-19. Google Scholar öffnen
- WASSERSTEIN, R.L., A.L. SCHIRM and N.A. LAZAR (2019b): Statistical Inference in the 21st Century: A World Beyond p < 0.05. In: The American Statistician 73 (sup1). Google Scholar öffnen
- WELLEK, S. (2017): A critical evaluation of the current "p-value controversy". In: Biometrical journal. Bi-ometrische Zeitschrift 59 (5): 854-872. Google Scholar öffnen
- WHITE, P. and S. GORARD (2021): Against Inferential Sta-tistics. How and why current statistics teaching gets it wrong. In: Statistics Education Research Journal 16 (1): 55-65. Google Scholar öffnen
- WILKINSON, M. (2013): Testing the null hypothesis: the forgotten legacy of Karl Popper? In: Journal of sports sciences 31 (9): 919-920. Google Scholar öffnen
- WINSHIP, C. and R.D. MARE (1992): Models for Sample Selection Bias. In: Annual Review of Sociology 18 (8): 327-350. Google Scholar öffnen
Zitation
Kein Zugriff
Sie haben keinen Zugriff auf diesen Inhalt.