Search results for "p-value"
showing 10 items of 23 documents
Assessment of Susceptibility Risk Factors for ADHD in Imaging Genetic Studies
2019
Objective: ADHD consists of a count of symptoms that often presents heterogeneity due to overdispersion and excess of zeros. Statistical inference is usually based on a dichotomous outcome that is underpowered. The main goal of this study was to determine a suited probability distribution to analyze ADHD symptoms in Imaging Genetic studies. Method: We used two independent population samples of children to evaluate the consistency of the standard probability distributions based on count data for describing ADHD symptoms. Results: We showed that the zero-inflated negative binomial (ZINB) distribution provided the best power for modeling ADHD symptoms. ZINB reveals a genetic variant, rs273342…
A significant p value is not equivalent to the superiority of one test index over another
2019
Background In patients with septic shock, the skin is often chosen for the evaluation of peripheral perfusion and oxygenation. Changes in skin microcirculatory vessel oxygen saturation and relative hemoglobin concentration can be described using a mottling score or captured with hyperspectral imaging. However, the effectiveness of the mottling score in assessing microcirculation remains to be shown. We hypothesize that the mottling score in patients with septic shock is related to skin microcirculatory perfusion indices quantified by hyperspectral imaging, biomarkers that reflect endothelium activation and damage, and clinical outcome. Methods Hyperspectral imaging of the knee area was perf…
Beyond psychology: prevalence of p value and confidence interval misinterpretation across different fields
2020
P values and confidence intervals (CIs) are the most widely used statistical indices in scientific literature. Several surveys have revealed that these two indices are generally misunderstood. However, existing surveys on this subject fall under psychology and biomedical research, and data from other disciplines are rare. Moreover, the confidence of researchers when constructing judgments remains unclear. To fill this research gap, we surveyed 1,479 researchers and students from different fields in China. Results reveal that for significant (i.e., p < .05, CI does not include zero) and non-significant (i.e., p > .05, CI includes zero) conditions, most respondents, regardless of acade…
Manipulating the alpha level cannot cure significance testing
2018
We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple in…
Predicting the Significance of Necessity
2019
With Necessary Condition Analysis (NCA), a necessity effect is estimated by calculating the amount of empty space in the upper-left corner in a plot with a predictor X and an outcome Y, and recently a method for testing the statistical significance of the necessity effect through permutation has been proposed. In the present simulation study, this method was found to give significant results already with a very weak true population necessity effect, i.e., exhibit high power, unless the sample size is very small. However, in some situations the significance of the necessity effect tends to increase with increased degree of sufficiency, which is paradoxical for a method whose objective is to …
P-Value, Confidence Intervals, and Statistical Inference: A New Dataset of Misinterpretation
2017
Statistical inference is essential for science since the twentieth century (Salsburg, 2001). Since it's introduction into science, the null hypothesis significance testing (NHST), in which the P-value serves as the index of “statistically significant,” is the most widely used statistical method in psychology (Sterling et al., 1995; Cumming et al., 2007), as well as other fields (Wasserstein and Lazar, 2016). However, surveys consistently showed that researchers in psychology may not able to interpret P-value and related statistical procedures correctly (Oakes, 1986; Haller and Krauss, 2002; Hoekstra et al., 2014; Badenes-Ribera et al., 2016). Even worse, these misinterpretations of P-value …
Is the p-Value a Suitable Basis for the Construction of Measures of Evidence? Comment on “The Role of p-Values in Judging the Strength of Evidence an…
2020
Dr. Gibson has to be congratulated for having enriched the wealth of articles written in response to the ASA statement on p-values of 2016 by a valuable and thoughtful contribution. We particularly...
A Modification of Stone's Test for Trend for Binary Outcome
1998
STONE (1988) suggested the first isotonic regression estimator as a tool for drawing inferences on possibly increased cancer case counts among several subregions around a putative source. He assumed the case counts to be Poisson distributed and therefore introduced a rare disease assumption into his approach. However, when analyzing cross sectional data one would rather refer to prevalence estimates among these subregions around a point risk source (for example the origin of chemical fallout). Therefore we applied antitonic regression estimation in Binomial distributions to derive a test statistic and a p value to test for a possible trend in the observed prevalence data around the putative…
PValues for Composite Null Models
2000
Abstract The problem of investigating compatibility of an assumed model with the data is investigated in the situation when the assumed model has unknown parameters. The most frequently used measures of compatibility are p values, based on statistics T for which large values are deemed to indicate incompatibility of the data and the model. When the null model has unknown parameters, p values are not uniquely defined. The proposals for computing a p value in such a situation include the plug-in and similar p values on the frequentist side, and the predictive and posterior predictive p values on the Bayesian side. We propose two alternatives, the conditional predictive p value and the partial…
Tests for Differentiation in Gene Expression Using a Data-Driven Order or Weights for Hypotheses
2005
In the analysis of gene expression by microarrays there are usually few subjects, but high-dimensional data. By means of techniques, such as the theory of spherical tests or with suitable permutation tests, it is possible to sort the endpoints or to give weights to them according to specific criteria determined by the data while controlling the multiple type I error rate. The procedures developed so far are based on a sequential analysis of weighted p-values (corresponding to the endpoints), including the most extreme situation of weighting leading to a complete order of p-values. When the data for the endpoints have approximately equal variances, these procedures show good power properties…