On Replicates for Comparing Species Densities in Space and Time

2021 ◽  
Vol 51 (2) ◽  
pp. 92-97
Author(s):  
Lee-Ann C. Hayek ◽  
Martin A. Buzas ◽  
Pamela Buzas-Stephens ◽  
Jeffrey S. Buzas

ABSTRACT Because benthic foraminifera exhibit spatial heterogeneity, a number of replicates or multiple biological samples are necessary to estimate population densities. In this study, we empirically examine the efficacy of taking four or fewer replicates to differentiate among mean densities in location and time using p-values as a metric for strength of evidence against the null hypothesis of no difference in taxon density. For spatial analyses, four stations along a traverse with four replicates per station were compared with ANOVA within Mission Bay, Texas, using the four most abundant taxa. The p-values for comparing mean densities among stations increased markedly for all taxa, as the number of samples per station decreased from four to two. Using a test level of 0.05, four replicates per station resulted, on average, in significant differences for three of four taxa, three replicates distinguished two of four taxa, and two replicates detected only one difference. For temporal analyses, a single station was sampled in the Indian River Lagoon, Florida, seasonally over four years. Again, p-values increased markedly as the number of samples per station decreased. Using a test level of 0.05, both four- and three-replicate groups were found to separate mean densities among the four years for three of four taxa, two replicates distinguished one taxon, and use of only one replicate could not detect any difference in mean densities among the four years. Based on these and previous field results, we recommend at least four replicates per station for environmental monitoring. However, when examining mean densities within larger ecological entities such as biofacies, just one sample at each station along a single traverse containing four stations in each bay could delineate Mission, Copano, and Mesquite bays in Texas.

Author(s):  
David McGiffin ◽  
Geoff Cumming ◽  
Paul Myles

Null hypothesis significance testing (NHST) and p-values are widespread in the cardiac surgical literature but are frequently misunderstood and misused. The purpose of the review is to discuss major disadvantages of p-values and suggest alternatives. We describe diagnostic tests, the prosecutor’s fallacy in the courtroom, and NHST, which involve inter-related conditional probabilities, to help clarify the meaning of p-values, and discuss the enormous sampling variability, or unreliability, of p-values. Finally, we use a cardiac surgical database and simulations to explore further issues involving p-values. In clinical studies, p-values provide a poor summary of the observed treatment effect, whereas the three- number summary provided by effect estimates and confidence intervals is more informative and minimises over-interpretation of a “significant” result. P-values are an unreliable measure of strength of evidence; if used at all they give only, at best, a very rough guide to decision making. Researchers should adopt Open Science practices to improve the trustworthiness of research and, where possible, use estimation (three-number summaries) or other better techniques.


Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1160
Author(s):  
Atsuko Okazaki ◽  
Sukanya Horpaopan ◽  
Qingrun Zhang ◽  
Matthew Randesi ◽  
Jurg Ott

Some genetic diseases (“digenic traits”) are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while the occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent pattern mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.


Harmful Algae ◽  
2021 ◽  
Vol 103 ◽  
pp. 102012
Author(s):  
Abdiel E. Laureano-Rosario ◽  
Malcolm McFarland ◽  
David J. Bradshaw ◽  
Jackie Metz ◽  
Rachel A. Brewton ◽  
...  

2021 ◽  
pp. 1-10
Author(s):  
Mansour H. Al-Askar ◽  
Fahad A. Abdullatif ◽  
Abdulmonem A. Alshihri ◽  
Asma Ahmed ◽  
Darshan Devang Divakar ◽  
...  

BACKGROUND AND OBJECTIVE: The aim of this study was to compare the efficacy of photobiomodulation therapy (PBMT) and photodynamic therapy (PDT) as adjuncts to mechanical debridement (MD) for the treatment of peri-implantitis. The present study is based on the null hypothesis that there is no difference in the peri-implant inflammatory parameters (modified plaque index [mPI], modified gingival index [mGI], probing depth [PD]) and crestal bone loss (CBL) following MD either with PBMT or PDT in patients with peri-implantitis. METHODS: Forty-nine patients with peri-implantitis were randomly categorized into three groups. In Groups 1 and 2, patients underwent MD with adjunct PBMT and PDT, respectively. In Group 3, patients underwent MD alone (controls). Peri-implant inflammatory parameters were measured at baseline and 3-months follow-up. P-values < 0.01 were considered statistically significant. RESULTS: At baseline, peri-implant clinicoradiographic parameters were comparable in all groups. Compared with baseline, there was a significant reduction in mPI (P< 0.001), mGI (P< 0.001) and PD (P< 0.001) in Groups 1 and 2 at 3-months follow-up. In Group 3, there was no difference in the scores of mPI, mGI and PD at follow-up. At 3-months follow-up, there was no difference in mPI, mGI and PD among patients in Groups 1 and 2. The mPI (P< 0.001), mGI (P< 0.001) and PD (P< 0.001) were significantly higher in Group 3 than Groups 1 and 2. The CBL was comparable in all groups at follow-up. CONCLUSION: PBMT and PDT seem to be useful adjuncts to MD for the treatment of peri-implant soft-tissue inflammation among patients with peri-implantitis.


Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 26 ◽  
Author(s):  
David Trafimow

There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone.


2007 ◽  
Vol 3 ◽  
pp. 117693510700300
Author(s):  
Yingye Zheng ◽  
Margaret Pepe

Consider a gene expression array study comparing two groups of subjects where the goal is to explore a large number of genes in order to select for further investigation a subset that appear to be differently expressed. There has been much statistical research into the development of formal methods for designating genes as differentially expressed. These procedures control error rates such as the false detection rate or family wise error rate. We contend however that other statistical considerations are also relevant to the task of gene selection. These include the extent of differential expression and the strength of evidence for differential expression at a gene. Using real and simulated data we first demonstrate that a proper exploratory analysis should evaluate these aspects as well as decision rules that control error rates. We propose a new measure called the mp-value that quantifies strength of evidence for differential expression. The mp-values are calculated with a resampling based algorithm taking into account the multiplicity and dependence encountered in microarray data. In contrast to traditional p-values our mp-values do not depend on specification of a decision rule for their definition. They are simply descriptive in nature. We contrast the mp-values with multiple testing p-values in the context of data from a breast cancer prognosis study and from a simulation model.


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