Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-23T21:08:21.686Z Has data issue: false hasContentIssue false
  • English
  • Français

Does Blind Tasting Work? Another Look

Published online by Cambridge University Press:  23 October 2019

Kevin W. Capehart Open the ORCID record for Kevin W. Capehart [Opens in a new window]
Kevin W. Capehart*
Affiliation:
Department of Economics, California State University, Fresno, CA, 93740; e-mail: kcapehart@csufresno.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

A study entitled “Does Blind Tasting Work? Investigating the Impact of Training on Blind Tasting Accuracy and Wine Preference,” published in the Proceedings issues of this journal, analyzed the effects of training on blind wine tasting accuracy (Wang and Prešern, 2018). I point out two issues with that study and reanalyze their data. I find that the effects of training on accuracy are small, even without controlling for self-selection bias that may produce upwardly biased estimates. To the extent training works, it does not seem to work well and it may only work as a selection device. (JEL Classifications: C91, D83, L66)

Keywords

blind wine tastingtrainingwine expertise
Type
Shorter Papers and Comments
Information
Journal of Wine Economics , Volume 14 , Issue 3 , August 2019 , pp. 309 - 320
Copyright
Copyright © American Association of Wine Economists 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

The author thanks Oxford University's Qian Wang for providing data from their study and answering questions about the design of their study. The author thanks without implicating the editor, an anonymous reviewer, and Elliott Morss for comments on earlier versions of this paper. The author declares that he has no relevant or material financial interests related to the research described in this paper. Data and code for replicating the results of this paper are available as supplementary files.

References

Almenberg, J., Dreber, A., and Goldstein, R. (2014). Hide the label, hide the difference? American Association of Wine Economics, Working Paper No. 165, August. Available from http://www.wine-economics.org/aawe/wp-content/uploads/2014/08/AAWE_WP165.pdf.Google Scholar
Ashton, R. H. (2017). Dimensions of expertise in wine evaluation. Journal of Wine Economics, 12(1), 59–83.CrossRefGoogle Scholar
Bodington, J. C. (2017). The distribution of ratings assigned to blind replicates. Journal of Wine Economics, 12(4), 363–369.CrossRefGoogle Scholar
Capehart, K. W., and Berg, E. C. (2018). Fine water: A blind taste test. Journal of Wine Economics, 13(1), 20–40.CrossRefGoogle Scholar
Clemens, M. A. (2017). The meaning of failed replications: A review and proposal. Journal of Economic Surveys, 31(1), 326–342.CrossRefGoogle Scholar
Cohen, J. (1992). A power primer. Psychological Bulletin, 111(1), 155–159.CrossRefGoogle Scholar
The Economist. (2017). Think wine connoisseurship is nonsense? Blind-tasting data suggest otherwise. 17 May, Economist.com. Available at https://www.economist.com/graphic-detail/2017/05/17/think-wine-connoisseurship-is-nonsense-blind-tasting-data-suggest-otherwise (accessed 17 May 2017).Google Scholar
Quandt, R. (2018). Book review of Segal, J., 2013, reds, whites & varsity blues: 65 years of the Oxford & Cambridge blind wine-tasting competition. Journal of Wine Economics, 13(1), 108–111.CrossRefGoogle Scholar
Real Sports with Bryan Gumbel. (2019). Oxford vs. Cambridge varsity wine tasting contest clip. Available at https://youtu.be/eTQuDRpGZds (accessed 19 April 2019).Google Scholar
Robinson, J. (2018). Oxford trumps Cambridge again, but next year…? 21 February, Jancisrobinson.com. Available at https://www.jancisrobinson.com/articles/oxford-trumps-cambridge-again-but-next-year (accessed 21 February 2018).Google Scholar
Ronek, D. W. (1991). Using logit coefficients to obtain the effects of independent variables on changes in probabilities. Social Forces, 70(2), 509–518.CrossRefGoogle Scholar
Sarsons, H. (2015). Rainfall and conflict: A cautionary tale. Journal of Development Economics, 115(July), 62–72.CrossRefGoogle Scholar
Schulz, K. F., and Grimes, D. A. (2002). Sample size slippages in randomised trials: Exclusions and the lost and wayward. The Lancet, 359(9308), 781–785.CrossRefGoogle ScholarPubMed
Semykina, A., and Wooldridge, J. M. (2018) Binary response panel data models with sample selection and self-selection. Journal of Applied Econometrics, 33(2), 179–197.CrossRefGoogle Scholar
Storchmann, K. (2012). Wine economics. Journal of Wine Economics, 7(1), 1–33.CrossRefGoogle Scholar
Wang, Q. J. (2018). Presentation at 12th Annual American Association of Wine Economists (AAWE) Conference in Ithaca, NY. Available at https://youtu.be/1-JpdnClGU0 (accessed 4 November 2018).Google Scholar
Wang, Q. J., and Prešern, D. (2018). Does blind tasting work? Investigating the impact of training on blind tasting accuracy and wine preference. Journal of Wine Economics, 13(4), 384–393.CrossRefGoogle Scholar
Young, A. (2019). Consistency without inference: Instrumental variables in practical application. London School of Economics Working Paper, August. Available at https://personal.lse.ac.uk/YoungA/ConsistencyWithoutInference.pdf (accessed 10 April 2019).Google Scholar
Ziliak, S., and McCloskey, D. N. (2008). The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives. Ann Arbor, MI: University of Michigan Press.Google Scholar
Supplementary material: File

Capehart supplementary material

Capehart supplementary material

Download Capehart supplementary material(File)
File 620.3 KB