Perspective on Beyond Statistical Significance: Finding Meaningful Effects

Stroke unit care: beyond the statistical significance

The Effect Size: Beyond Statistical Significance

The branch of statistics known as inferential statistics tries to find out information about a population by studying a representative sample of that population. The main tools of inferential statistics are the statistical significance tests. Every statistical significance test includes two hypotheses; the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis assumes tha...

Beyond Statistical Significance: Implications of Network Structure on Neuronal Activity

It is a common and good practice in experimental sciences to assess the statistical significance of measured outcomes. For this, the probability of obtaining the actual results is estimated under the assumption of an appropriately chosen null-hypothesis. If this probability is smaller than some threshold, the results are deemed statistically significant and the researchers are content in having...

Finding Meaningful Bayesian Confirmation Measures

The paper focuses on Bayesian confirmation measures used for evaluation of rules induced from data. To distinguish between many confirmation measures, their properties are analyzed. The article considers a group of symmetry properties. We demonstrate that the symmetry properties proposed in the literature focus on extreme cases corresponding to entailment or refutation of the rule’s conclusion ...

On Statistical Significance of Signal

A definition for the statistical significance of a signal in an experiment is proposed by establishing a correlation between the observed p-value and the normal distribution integral probability, which is suitable for both counting experiment and continuous test statistics. The explicit expressions to calculate the statistical significance for both cases are given.