New polystochastic statistical inference in social sciences - defining new rules and thresholdsOpić, Siniša (Avtor) Bayesianeffect sizeNHSTp-valuepolystochasticsocial sciencestatistical inferenceThe Null Hypothesis Significance Testing (NHST) framework has sparked considerable debate within the scientific community, leading to numerous studies advocating for a re-evaluation of the current system. New polystochastic statistical inference defines methods of statistical inference that integrate rules and thresholds for both rejecting the null hypothesis and confirming the alternative hypothesis. This approach unifies the control of respondents' influence on statistical significance and introduces criteria such as effect size and Bayesian inference for confirming the alternative hypothesis. Unlike NHST, polystochastic statistical inference controls Type I error (p-value) and aims to optimize the confirmation of evidence without increasing the risk of Type II errors.20252025-07-18 12:52:10Znanstveno delo93766UDK: 303:311.21COBISS_ID: 239060739DOI: 10.18690/rei.4907ISSN pri članku: 1855-4431sl