scholarly journals Pragmatic Hypotheses in the Evolution of Science

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 883 ◽  
Author(s):  
Luis Gustavo Esteves ◽  
Rafael Izbicki ◽  
Julio Michael Stern ◽  
Rafael Bassi Stern
Keyword(s):  
Hypothesis Testing ◽  
Statistical Hypothesis ◽  
Scientific Theories ◽  
Statistical Hypothesis Testing ◽  
Hypothesis Tests ◽  
Experimental Conditions ◽  
Modal Operators ◽  
Evolution Of Science ◽  
Consistent Tests

This paper introduces pragmatic hypotheses and relates this concept to the spiral of scientific evolution. Previous works determined a characterization of logically consistent statistical hypothesis tests and showed that the modal operators obtained from this test can be represented in the hexagon of oppositions. However, despite the importance of precise hypothesis in science, they cannot be accepted by logically consistent tests. Here, we show that this dilemma can be overcome by the use of pragmatic versions of precise hypotheses. These pragmatic versions allow a level of imprecision in the hypothesis that is small relative to other experimental conditions. The introduction of pragmatic hypotheses allows the evolution of scientific theories based on statistical hypothesis testing to be interpreted using the narratological structure of hexagonal spirals, as defined by Pierre Gallais.

scholarly journals Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference

2019 ◽  
Vol 1 (3) ◽  
pp. 945-961 ◽  
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Artificial Intelligence ◽  
Quantitative Analysis ◽  
Hypothesis Testing ◽  
Statistical Inference ◽  
Data Science ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Hypothesis Tests ◽  
Analysis Methods ◽  
Procedural Components

Statistical hypothesis testing is among the most misunderstood quantitative analysis methods from data science. Despite its seeming simplicity, it has complex interdependencies between its procedural components. In this paper, we discuss the underlying logic behind statistical hypothesis testing, the formal meaning of its components and their connections. Our presentation is applicable to all statistical hypothesis tests as generic backbone and, hence, useful across all application domains in data science and artificial intelligence.

scholarly journals The Epistemic and Pragmatic Function of Dichotomous Claims Based on Statistical Hypothesis Tests

2021 ◽  
Author(s):  
Duygu Uygun Tunç ◽  
Mehmet Necip Tunç ◽  
Daniel Lakens
Keyword(s):  
Hypothesis Testing ◽  
Decision Procedures ◽  
Data Models ◽  
Statistical Hypothesis ◽  
Test Statistics ◽  
Statistical Hypothesis Testing ◽  
Hypothesis Tests ◽  
Universal Statement ◽  
Pragmatic Function ◽  
Continuous Test

Researchers commonly make dichotomous claims based on continuous test statistics. Many have branded the practice as misuse of statistics, and criticize scientists for suffering from “dichotomania”. However, the role dichotomous claims play in science is not primarily a statistical one, but an epistemological and pragmatic one. The epistemological function of dichotomous claims consists in transforming data into factual statements that can falsify a universal statement. This transformation requires pre-specified methodological decision procedures such as statistical hypothesis testing (e.g., Neyman-Pearson tests). From the perspective of methodological falsificationism these decision procedures are necessary, as probabilistic statements (e.g. continuous test statistics) cannot function as falsifiers of substantive hypotheses. However, they are not sufficient since for dichotomous claims to have any implication regarding theoretical claims about phenomena, there should be a valid derivation chain linking theoretical, experimental and data models. The pragmatic function of dichotomous claims is facilitating scrutiny and criticism among peers by generating contestable statements, a process referred to by Popper as 'conjectures and refutations', through which we can determine which theories withstand scrutiny the best. Abandoning dichotomous claims to combat the misuse of statistics would not improve scientific inferences but will sacrifice these crucial epistemic and pragmatic functions.

scholarly journals Normalisr: normalization and association testing for single-cell CRISPR screen and co-expression

2021 ◽  
Author(s):  
Lingfei Wang
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Large Scale ◽  
High Sensitivity ◽  
Statistical Hypothesis ◽  
P Value ◽  
Statistical Hypothesis Testing ◽  
Experimental Conditions ◽  
Library Size ◽  
Association Testing

AbstractSingle-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Here we present Normalisr, a linear-model-based normalization and statistical hypothesis testing framework that unifies single-cell differential expression, co-expression, and CRISPR scRNA-seq screen analyses. By systematically detecting and removing nonlinear confounding from library size, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased P-value estimation. We use Normalisr to reconstruct robust gene regulatory networks from trans-effects of gRNAs in large-scale CRISPRi scRNA-seq screens and gene-level co-expression networks from conventional scRNA-seq.

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