Multiple comparisons tests (MCTs) are performed several times on the mean of experimental conditions. When the null hypothesis is rejected in a validation, MCTs are performed when certain ...experimental conditions have a statistically significant mean difference or there is a specific aspect between the group means. A problem occurs if the error rate increases while multiple hypothesis tests are performed simultaneously. Consequently, in an MCT, it is necessary to control the error rate to an appropriate level. In this paper, we discuss how to test multiple hypotheses simultaneously while limiting type I error rate, which is caused by α inflation. To choose the appropriate test, we must maintain the balance between statistical power and type I error rate. If the test is too conservative, a type I error is not likely to occur. However, concurrently, the test may have insufficient power resulted in increased probability of type II error occurrence. Most researchers may hope to find the best way of adjusting the type I error rate to discriminate the real differences between observed data without wasting too much statistical power. It is expected that this paper will help researchers understand the differences between MCTs and apply them appropriately.
In most clinical and experimental studies, the standard deviation (SD) and the estimated standard error of the mean (SEM) are used to present the characteristics of sample data and to explain ...statistical analysis results. However, some authors occasionally muddle the distinctive usage between the SD and SEM in medical literature. Because the process of calculating the SD and SEM includes different statistical inferences, each of them has its own meaning. SD is the dispersion of data in a normal distribution. In other words, SD indicates how accurately the mean represents sample data. However the meaning of SEM includes statistical inference based on the sampling distribution. SEM is the SD of the theoretical distribution of the sample means (the sampling distribution). While either SD or SEM can be applied to describe data and statistical results, one should be aware of reasonable methods with which to use SD and SEM. We aim to elucidate the distinctions between SD and SEM and to provide proper usage guidelines for both, which summarize data and describe statistical results.
Manuscripts submitted to journals should be understandable even to those who are not experts in a particular field. Moreover, they should use publicly available materials and the results should be ...verifiable and reproducible. Readers and reviewers will want to check the strengths and weaknesses of the research study design, and ways to make this determination should be clear through proper analysis methods. Studies should be described in detail so as to help readers understand the results. Statistical analysis is one of the key methods by which to do this. The inappropriate application of statistical methods could be misleading to readers and clinicians. While many researchers describe their general research methods in detail, statistical methods tend to be described briefly, with certain omissions or errors or other incorrect aspects. For instance, researchers should describe whether the median or mean was used, whether parametric or nonparametric tests were used, whether the data meet the normality test, whether confounding factors were corrected, and whether stratification or matching methods were used. Statistical analysis regardless of the program should be reported correctly. The results may be less reliable if the statistical assumptions before applying the statistical method are not met. These common errors in statistical methods originate from the researcher's lack of knowledge of statistics and/or from the lack of any statistical consultation. The aim of this work is to help researchers know what is important statistically and how to present it in papers.
Quantitative trait loci (QTL) analyses for five groups of hormones, including cytokinins in Arabidopsis roots were performed using recombinant inbred lines (Ler×Cvi). Significant QTLs were detected ...for cytokinins, jasmonic acid and salicylic acid. Separate analysis of two sub-populations, viz., vegetative and flowering plants revealed that many of the QTLs were development-specific. Using near-isogenic lines, several significant QTLs were confirmed; three co-localized QTL regions were responsible for determining several cytokinin metabolites. Using a knock-out plant, a functional role of zeatin N-glucosyltransferase gene (UGT76C2) underlying a large-effect QTL for levels of tZ-N-glucosides and tZRMP was evaluated in the metabolism of cytokinins. Pleotropic effects of this gene were found for cytokinin levels in both roots and leaves, but significant changes of morphological traits were observed only in roots. Hormone QTL analysis reveals development-specific and organ-dependent aspects of the regulation of plant hormone content and metabolism.
Statistical data presentation In, Junyong; Lee, Sangseok
Korean journal of anesthesiology,
06/2017, Letnik:
70, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Data are usually collected in a raw format and thus the inherent information is difficult to understand. Therefore, raw data need to be summarized, processed, and analyzed. However, no matter how ...well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers. In this article, the techniques of data and information presentation in textual, tabular, and graphical forms are introduced. Text is the principal method for explaining findings, outlining trends, and providing contextual information. A table is best suited for representing individual information and represents both quantitative and qualitative information. A graph is a very effective visual tool as it displays data at a glance, facilitates comparison, and can reveal trends and relationships within the data such as changes over time, frequency distribution, and correlation or relative share of a whole. Text, tables, and graphs for data and information presentation are very powerful communication tools. They can make an article easy to understand, attract and sustain the interest of readers, and efficiently present large amounts of complex information. Moreover, as journal editors and reviewers glance at these presentations before reading the whole article, their importance cannot be ignored.
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