China
Africa
Explore chapters and articles related to this topic
Statistics You Need
Published in Saif Aldeen Saleh AlRyalat, Shaher Momani, A Beginner's Guide to Using Open Access Data, 2019
The Kruskal-Wallis H test can be thought of as the nonparametric version of the one-way ANOVA test, and is also called one-way ANOVA on ranks. You may consider the one-way ANOVA when you want to find if there is a significant difference between a continuous dependent variable and an ordinal (or multinominal) independent variable and your data do not meet the normality assumption of the one-way ANOVA, or when your dependent variable is ordinal (but you can use it with continuous dependent variable). As in a one-way ANOVA, if you are interested in knowing which groups are statistically significant from each other, you need to run a post hoc analysis using Dunn's procedure with the Bonferroni adjustment. This test analyzes the subgroups when you find that there is a difference between three or more groups, but you want to know exactly between which two subgroups the difference is most significant. By default, SPSS will perform this post hoc analysis if you have a significant Kruskal-Wallis H test, and it will produce the results under “pairwise comparison.”
Biostatistics: Issues in study design, analysis, and reporting
Published in Stephen W. Gutkin, Writing High-Quality Medical Publications, 2018
Also termed the one-way ANOVA on ranks, the K–W test is a nonparametric statistical assessment of ranked data. This method can be used to assess differences between at least two experimental arms of an IV or a DV that is either ordinal or continuous. In some ways, it can be interpreted as (1) the Wilcoxon RST extended to at least three groups; and (2) a nonparametric analogue of a one-way ANOVA. The K–W test may also be useful when one-way ANOVA assumptions are not met.13
Antiproliferative Effects of Thymoquinone in MCF-7 Breast and HepG2 Liver Cancer Cells: Possible Role of Ceramide and ER Stress
Published in Nutrition and Cancer, 2021
Mutay Aslan, Ebru Afşar, Esma Kırımlıoglu, Tuğçe Çeker, Çağatay Yılmaz
Statistical analysis was performed using SigmaStat statistical software version 2.0 (Sigma, St. Louis, MO, USA). Statistical analysis for each measurement is described in figure and table legends. To compare the groups via the SigmaStat statistical sofware, we first performed a normality test. A test that passed indicated that the data matched the pattern expected if the data was drawn from a population with a normal distribution. If the sample data were not normally distributed, the normality test failed. In such case the software performed a nonparametric test. The experimental groups were compared by either One Way ANOVA (analysis of variance) or by Kruskal-Wallis One Way ANOVA on Ranks. When there was a statistically significant difference, we used multiple comparison procedures also known as post-hoc tests to determine exactly which groups were different.
Sex steroid hormone receptor expression in the vaginal wall in cervical cancer survivors after radiotherapy
Published in Acta Oncologica, 2019
Alexandra Hofsjö, Nina Bohm-Starke, Karin Bergmark, Britt Masironi, Lena Sahlin
The main analyses were comparisons between cancer survivors and control women using the Mann–Whitney U test. Thereafter subgroup-analyses were performed comparing hormonal levels in cancer survivors with various hormonal treatments and controls in different phases of the menstrual cycle and those on oral contraceptives. Further subgroup-analyses were performed for the hormone receptor expression in the two treatment groups of cancer survivors. ANOVA on ranks (Kruskal–Wallis test) was used for comparing differences in more than two treatment groups and significances were evaluated by Dunn’s test, all pairwise multiple comparison procedures (Sigma-Plot 13, Alfasoft AB, Gothenburg, Sweden). According to our statistic consultant, the Bonferroni adjustments are appropriate when the study variables are independent. In our study, the variables are not independent, hormones affect other hormones and receptors, and therefore adjustments according to the Bonferroni will be too conservative and result in type-II errors, and are therefore not performed. Correlations were evaluated by Spearman’s test. Values were considered significantly different when p< .05.
Milk-whey diet substantially suppresses seizure-like phenotypes of paraShu, a Drosophila voltage-gated sodium channel mutant
Published in Journal of Neurogenetics, 2019
Junko Kasuya, Atulya Iyengar, Hung-Lin Chen, Patrick Lansdon, Chun-Fang Wu, Toshihiro Kitamoto
Statistical comparisons between two groups were performed using the two-tailed student’s t-test assuming unequal variance or, for non-normally distributed data, the Mann–Whitney U test. For multiple groups displaying a normal distribution, statistical significance was determined using one-way ANOVA, with Bonferroni t-test comparisons between control and treatment groups post hoc. For data exhibiting non-normal distributions, the Kruskal–Wallis one-way ANOVA on ranks test was performed. Comparisons among groups or among groups and a control were calculated using Dunn’s method post hoc. Data not conforming to a normal distribution are presented as box-and-whisker plots showing values of the first, second, and third quartiles (box), as well as the 10th and 90th percentiles (whisker), unless otherwise stated. Two-way repeated measures ANOVA and Holm–Sidak multiple comparisons were used to analyze temperature-induced behavioral phenotypes. χ2 test was used to analyze the effect of diet on male’s mating success. Kaplan–Meier Survival Analysis (Log-Rank) was used to evaluate the results of lifespan experiments. Statistical analyses were performed using SigmaPlot for Windows Version 13 (Systat Software, Inc., Point Richmond, CA).