ArticleCureus2023
A Framework to Avoid Significance Fallacy.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This manuscript presents a concise approach to tackle the widespread misuse of statistical significance in scientific research, focusing on public health. It offers practical guidance for conducting accurate statistical evaluations and promoting easily understandable results based on actual evidence. When conducting a statistical study to inform decision-making, it is recommended to follow a step-by-step sequence while considering various factors. Firstly, multiple target hypotheses should be adopted to assess the compatibility of experimental data with different models. Reporting all P-values in full, rounded in order to have a single non-zero significant digit, enhances transparency and reduces the likelihood of exaggerating the state of the evidence. Detailed documentation of the procedures used to evaluate the compatibility between test assumptions and data should be provided for rigorous assessment. A descriptive evaluation of results can be aided by using statistical compatibility ranges, which help avoid misrepresenting the evidence. Separately evaluating and reporting statistical compatibility and effect size prevents the magnitude fallacy. Additionally, reporting measures of statistical effect size enables evaluation of sectoral relevance, such as clinical significance. Multiple compatibility intervals, such as 99%, 95%, and 90% confidence intervals, should be reported to allow readers to assess the variation of P-values based on the width of the interval. These recommendations aim to enhance the robustness and interpretability of statistical analyses and promote transparent reporting of findings. The author encourages journal adoption of similar frameworks to enhance scientific rigor, particularly in the field of medical science.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.