• search hit 78 of 102
Back to Result List

How to embrace variation and accept uncertainty in linguistic and psycholinguistic data analysis

  • The use of statistical inference in linguistics and related areas like psychology typically involves a binary decision: either reject or accept some null hypothesis using statistical significance testing. When statistical power is low, this frequentist data-analytic approach breaks down: null results are uninformative, and effect size estimates associated with significant results are overestimated. Using an example from psycholinguistics, several alternative approaches are demonstrated for reporting inconsistencies between the data and a theoretical prediction. The key here is to focus on committing to a falsifiable prediction, on quantifying uncertainty statistically, and learning to accept the fact that - in almost all practical data analysis situations - we can only draw uncertain conclusions from data, regardless of whether we manage to obtain statistical significance or not. A focus on uncertainty quantification is likely to lead to fewer excessively bold claims that, on closer investigation, may turn out to be not supported byThe use of statistical inference in linguistics and related areas like psychology typically involves a binary decision: either reject or accept some null hypothesis using statistical significance testing. When statistical power is low, this frequentist data-analytic approach breaks down: null results are uninformative, and effect size estimates associated with significant results are overestimated. Using an example from psycholinguistics, several alternative approaches are demonstrated for reporting inconsistencies between the data and a theoretical prediction. The key here is to focus on committing to a falsifiable prediction, on quantifying uncertainty statistically, and learning to accept the fact that - in almost all practical data analysis situations - we can only draw uncertain conclusions from data, regardless of whether we manage to obtain statistical significance or not. A focus on uncertainty quantification is likely to lead to fewer excessively bold claims that, on closer investigation, may turn out to be not supported by the data.show moreshow less

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author details:Shravan VasishthORCiDGND, Andrew GelmanORCiDGND
DOI:https://doi.org/10.1515/ling-2019-0051
ISSN:0024-3949
ISSN:1613-396X
Title of parent work (English):Linguistics : an interdisciplinary journal of the language sciences
Publisher:De Gruyter Mouton
Place of publishing:Berlin
Publication type:Article
Language:English
Date of first publication:2021/09/07
Publication year:2021
Release date:2023/09/19
Tag:experimental linguistics; inference; statistical; statistical data analysis; uncertainty quantification
Volume:59
Issue:5
Number of pages:32
First page:1311
Last Page:1342
Organizational units:Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Linguistik
DDC classification:4 Sprache / 40 Sprache / 400 Sprache
Peer review:Referiert
License (German):License LogoCC-BY - Namensnennung 4.0 International
Accept ✔
This website uses technically necessary session cookies. By continuing to use the website, you agree to this. You can find our privacy policy here.