scholarly journals Comparison between 2000 and 2018 on the reporting of statistical significance and clinical relevance in physiotherapy clinical trials in six major physiotherapy journals: a meta-research design

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e054875
Author(s):  
Arianne Verhagen ◽  
Peter William Stubbs ◽  
Poonam Mehta ◽  
David Kennedy ◽  
Anthony M Nasser ◽  
...  
Keyword(s):  
Clinical Relevance ◽  
Research Study ◽  
Statistical Significance ◽  
Group Analysis ◽  
Group Differences ◽  
Statistical Significance Testing ◽  
P Values ◽  
High Prevalence ◽  
Study Characteristics ◽  
Randomised Controlled

DesignMeta-research.ObjectiveTo compare the prevalence of reporting p values, effect estimates and clinical relevance in physiotherapy randomised controlled trials (RCTs) published in the years 2000 and 2018.MethodsWe performed a meta-research study of physiotherapy RCTs obtained from six major physiotherapy peer-reviewed journals that were published in the years 2000 and 2018. We searched the databases Embase, Medline and PubMed in May 2019, and extracted data on the study characteristics and whether articles reported on statistical significance, effect estimates and confidence intervals for baseline, between-group, and within-group differences, and clinical relevance. Data were presented using descriptive statistics and inferences were made based on proportions. A 20% difference between 2000 and 2018 was regarded as a meaningful difference.ResultsWe found 140 RCTs: 39 were published in 2000 and 101 in 2018. Overall, there was a high prevalence (>90%) of reporting p values for the main (between-group) analysis, with no difference between years. Statistical significance testing was frequently used for evaluating baseline differences, increasing from 28% in 2000 to 61.4% in 2018. The prevalence of reporting effect estimates, CIs and the mention of clinical relevance increased from 2000 to 2018 by 26.6%, 34% and 32.8% respectively. Despite an increase in use in 2018, over 40% of RCTs failed to report effect estimates, CIs and clinical relevance of results.ConclusionThe prevalence of using p values remains high in physiotherapy research. Although the proportion of reporting effect estimates, CIs and clinical relevance is higher in 2018 compared to 2000, many publications still fail to report and interpret study findings in this way.

scholarly journals Twenty steps towards an adequate inferential interpretation of p-values

2018 ◽  
Author(s):  
Norbert Hirschauer ◽  
Sven Grüner ◽  
Oliver Mußhoff ◽  
Claudia Becker
Keyword(s):  
Regression Analysis ◽  
Bayesian Statistics ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Statistical Significance ◽  
Significance Testing ◽  
Statistical Significance Testing ◽  
P Values ◽  
Logical Consistency ◽  
Categorical Reasoning

We suggest twenty immediately actionable steps to reduce widespread inferential errors related to “statistical significance testing.” Our propositions refer first to the theoretical preconditions for using p-values. They furthermore include wording guidelines as well as structural and operative advice of how to present results, especially in multiple regression analysis. Our propositions aim at fostering the logical consistency of inferential arguments by avoiding false categorical reasoning. They are not aimed at dispensing with p-values or completely replacing frequentist approaches by Bayesian statistics.

scholarly journals Twenty steps towards an adequate inferential interpretation of p-values

2017 ◽  
Author(s):  
Norbert Hirschauer ◽  
Oliver Mußhoff ◽  
Claudia Becker ◽  
Sven Grüner
Keyword(s):  
Regression Analysis ◽  
Bayesian Statistics ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Statistical Significance ◽  
Significance Testing ◽  
Statistical Significance Testing ◽  
P Values ◽  
Logical Consistency ◽  
Categorical Reasoning

We suggest twenty immediately actionable steps to reduce widespread inferential errors related to “statistical significance testing.” Our propositions refer first to the theoretical preconditions for using p-values. They furthermore include wording guidelines as well as structural and operative advice of how to present results, especially in multiple regression analysis. Our propositions aim at fostering the logical consistency of inferential arguments by avoiding false categorical reasoning. They are not aimed at dispensing with p-values or completely replacing frequentist approaches by Bayesian statistics.

Twenty Steps Towards an Adequate Inferential Interpretation of p-Values in Econometrics

2019 ◽  
Vol 239 (4) ◽  
pp. 703-721 ◽  
Author(s):  
Norbert Hirschauer ◽  
Sven Grüner ◽  
Oliver Mußhoff ◽  
Claudia Becker
Keyword(s):  
Regression Analysis ◽  
Bayesian Statistics ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Statistical Significance ◽  
Significance Testing ◽  
Statistical Significance Testing ◽  
P Values ◽  
Logical Consistency ◽  
Categorical Reasoning

Abstract We suggest twenty immediately actionable steps to reduce widespread inferential errors related to “statistical significance testing.” Our propositions refer to the theoretical preconditions for using p-values. They furthermore include wording guidelines as well as structural and operative advice on how to present results, especially in research based on multiple regression analysis, the working horse of empirical economists. Our propositions aim at fostering the logical consistency of inferential arguments by avoiding false categorical reasoning. They are not aimed at dispensing with p-values or completely replacing frequentist approaches by Bayesian statistics.

scholarly journals Twenty steps towards an adequate inferential interpretation of p-values

2018 ◽  
Author(s):  
Norbert Hirschauer ◽  
Sven Grüner ◽  
Oliver Mußhoff ◽  
Claudia Becker
Keyword(s):  
Regression Analysis ◽  
Bayesian Statistics ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Statistical Significance ◽  
Significance Testing ◽  
Statistical Significance Testing ◽  
P Values ◽  
Logical Consistency ◽  
Categorical Reasoning

We suggest twenty immediately actionable steps to reduce widespread inferential errors related to “statistical significance testing.” Our propositions refer first to the theoretical preconditions for using p-values. They furthermore include wording guidelines as well as structural and operative advice of how to present results, especially in multiple regression analysis. Our propositions aim at fostering the logical consistency of inferential arguments by avoiding false categorical reasoning. They are not aimed at dispensing with p-values or completely replacing frequentist approaches by Bayesian statistics.

scholarly journals Beyond ‘significance’: principles and practice of the Analysis of Credibility

2018 ◽  
Vol 5 (1) ◽  
pp. 171047 ◽  
Author(s):  
Robert A. J. Matthews
Keyword(s):  
Research Study ◽  
Statistical Significance ◽  
Research Question ◽  
Worked Examples ◽  
Significance Testing ◽  
Additional Insight ◽  
Statistical Significance Testing ◽  
New Findings ◽  
Replication Crisis ◽  
Study Designs

The inferential inadequacies of statistical significance testing are now widely recognized. There is, however, no consensus on how to move research into a ‘post p  < 0.05’ era. We present a potential route forward via the Analysis of Credibility, a novel methodology that allows researchers to go beyond the simplistic dichotomy of significance testing and extract more insight from new findings. Using standard summary statistics, AnCred assesses the credibility of significant and non-significant findings on the basis of their evidential weight, and in the context of existing knowledge. The outcome is expressed in quantitative terms of direct relevance to the substantive research question, providing greater protection against misinterpretation. Worked examples are given to illustrate how AnCred extracts additional insight from the outcome of typical research study designs. Its ability to cast light on the use of p -values, the interpretation of non-significant findings and the so-called ‘replication crisis’ is also discussed.

scholarly journals Twenty steps towards an adequate inferential interpretation of p-values

2019 ◽  
Author(s):  
Norbert Hirschauer ◽  
Sven Grüner ◽  
Oliver Mußhoff ◽  
Claudia Becker
Keyword(s):  
Regression Analysis ◽  
Bayesian Statistics ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Statistical Significance ◽  
Significance Testing ◽  
Statistical Significance Testing ◽  
P Values ◽  
Logical Consistency ◽  
Categorical Reasoning

We suggest twenty immediately actionable steps to reduce widespread inferential errors related to “statistical significance testing.” Our propositions refer first to the theoretical preconditions for using p-values. They furthermore include wording guidelines as well as structural and operative advice on how to present results, especially in research based on multiple regression analysis, the working horse of empirical economists. Our propositions aim at fostering the logical consistency of inferential arguments by avoiding false categorical reasoning. They are not aimed at dispensing with p-values or completely replacing frequentist approaches by Bayesian statistics.

scholarly journals Twenty steps towards an adequate inferential interpretation of p-values

2019 ◽  
Author(s):  
Norbert Hirschauer ◽  
Sven Grüner ◽  
Oliver Mußhoff ◽  
Claudia Becker
Keyword(s):  
Regression Analysis ◽  
Bayesian Statistics ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Statistical Significance ◽  
Significance Testing ◽  
Statistical Significance Testing ◽  
P Values ◽  
Logical Consistency ◽  
Categorical Reasoning

We suggest twenty immediately actionable steps to reduce widespread inferential errors related to “statistical significance testing.” Our propositions refer first to the theoretical preconditions for using p-values. They furthermore include wording guidelines as well as structural and operative advice on how to present results, especially in research based on multiple regression analysis, the working horse of empirical economists. Our propositions aim at fostering the logical consistency of inferential arguments by avoiding false categorical reasoning. They are not aimed at dispensing with p-values or completely replacing frequentist approaches by Bayesian statistics.

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