尽管杂志的知名度和最新的"作者信息",但统计报告不佳,数据呈现不足和转换仍然存在
Martin Héroux1,2, Joanna Diong2,3, Elizabeth Bye1,2
1School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, 2052, Australia.
F1000Research
|March 4, 2024
概括
公开强调不良的科学报告并没有改善实践. 期刊报告要求只是部分有效,这表明需要采取更强有力的措施来提高研究完整性和数据呈现.
科学领域:
- 科学出版业的科学出版.
- 研究完整性研究完整性
- 生物医学研究报告报告.
背景情况:
- 对研究的合理报告对于科学进步至关重要.
- 报告不良做法很普遍,尽管有编辑的干预,但经常持续存在.
- 之前的审计发现了神经生理学杂志的重大报告缺陷.
研究的目的:
- 评估公开强调关于期刊实践的糟糕报道的影响.
- 评估"神经生理学杂志"作者信息中新报告准则的执行情况.
- 为了确定要求与强烈鼓励的报告标准的有效性.
主要方法:
- 对2019-2020年发表的"神经生理学杂志"论文进行了审计.
- 分析2016年审计报告项目和新引入的指导方针.
- 使用总结统计数据 (平均值,计数) 来比较随时间推移的报告实践.
主要成果:
- 与2016年相比,一些报告实践保持不变或恶化.
- 观察到高错误率,包括标准错误的不正确使用 (60%) 和未定义的可变性指标 (23%).
- 遵守新引入的强制性做法很低 (34-37%),强烈鼓励的做法甚至更低 (9-26%).
结论:
- 公开揭露不良报告对改善研究报告质量的影响很小.
- 期刊要求和对报告实践的鼓励显示出有限的有效性.
- 需要采取更强有力的战略干预措施来解决科学研究报告中的广泛问题.
相关概念视频
Hardy-Weinberg Principle
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.In the early 20th century,...
Bias
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Statistical Analysis: Overview
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Systematic Error: Methodological and Sampling Errors
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Biostatistics: Overview
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Discrete variables are...
Bias in Epidemiological Studies
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:


