The Search for Significance: Seeing Your True Worth Through God's Eyes

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Lydia Denworth, "A Significant Problem: Standard scientific methods are under fire. Will anything change?", Scientific American, vol. 321, no. 4 (October 2019), pp.62–67. "The use of p values for nearly a century [since 1925] to determine statistical significance of experimental results has contributed to an illusion of certainty and [to] reproducibility crises in many scientific fields. There is growing determination to reform statistical analysis... Some [researchers] suggest changing statistical methods, whereas others would do away with a threshold for defining "significant" results." (p. 63.) If the p value is lower than the significance level, the results are interpreted as refuting the null hypothesis and reported as statistically significant. Next, you perform a t test to see whether actively smiling leads to more happiness. Using the difference in average happiness between the two groups, you calculate:

In a hypothesis test, the p value is compared to the significance level to decide whether to reject the null hypothesis. Starting in the 2010s, some journals began questioning whether significance testing, and particularly using a threshold of α=5%, was being relied on too heavily as the primary measure of validity of a hypothesis. [52] Some journals encouraged authors to do more detailed analysis than just a statistical significance test. In social psychology, the journal Basic and Applied Social Psychology banned the use of significance testing altogether from papers it published, [53] requiring authors to use other measures to evaluate hypotheses and impact. [54] [55] Neyman, J. (1937). "Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability". Philosophical Transactions of the Royal Society A. 236 (767): 333–380. Bibcode: 1937RSPTA.236..333N. doi: 10.1098/rsta.1937.0005. JSTOR 91337.

If the p value is higher than the significance level, the null hypothesis is not refuted, and the results are not statistically significant. In quantitative research, data are analyzed through null hypothesis significance testing, or hypothesis testing. This is a formal procedure for assessing whether a relationship between variables or a difference between groups is statistically significant. Null and alternative hypotheses On December 20, 1993, the General Assembly adopted the Declaration on the Elimination of Violence against Women, paving the path towards eradicating violence against women and girls worldwide. Further, on February 07, 2000, November 25 was officially designated as the International Day for the Elimination of Violence Against Women. Woolston, Chris (2015-03-05). "Psychology journal bans P values". Nature. 519 (7541): 9. Bibcode: 2015Natur.519....9W. doi: 10.1038/519009f.

a t value (the test statistic) that tells you how much the sample data differs from the null hypothesis, One by one, The Search for Significance confronts these lies, dismantles them, and points you to a higher truth that is the source of life's meaning. It points you to Almighty God--the source of life itself.Novella, Steven (February 25, 2015). "Psychology Journal Bans Significance Testing". Science-Based Medicine. McShane, Blake; Greenland, Sander; Amrhein, Valentin (March 2019). "Scientists rise up against statistical significance". Nature. 567 (7748): 305–307. Bibcode: 2019Natur.567..305A. doi: 10.1038/d41586-019-00857-9. PMID 30894741. The widespread abuse of statistical significance represents an important topic of research in metascience. [59] Redefining significance [ edit ] Bellhouse, P. (2001), "John Arbuthnot", in Statisticians of the Centuries by C.C. Heyde and E. Seneta, Springer, pp.39–42, ISBN 978-0-387-95329-8

Krzywinski, Martin; Altman, Naomi (30 October 2013). "Points of significance: Significance, P values and t-tests". Nature Methods. 10 (11): 1041–1042. doi: 10.1038/nmeth.2698. PMID 24344377. So far, we have discussed only the simplest case, in which our set of putative predictors are independent. Dependence among the predictors complicates matters—if one of the predictors is statistically significant by chance, then other correlated predictors are also more likely to be statistically significant, which may appear to add weight to the significant results. For example, we might have several correlated metabolites as predictors. When one is selected, others may also be pulled into the model as predictors, creating a readily interpretable (yet wrong) biological explanation. Sprent, P. (1989), Applied Nonparametric Statistical Methods (Seconded.), Chapman & Hall, ISBN 978-0-412-44980-2 Another common analysis in which P values can easily be misinterpreted is the selection of a prediction model for multiple regression or classification. To show how this can occur, we performed 1,000 simulations of our 10 physiological variables that were, as before, random and independent of each other and of SBP. We then applied forward selection to identify variables that statistically predicted SBP. In this selection process, we start with no variables in the model and iteratively add the variables that provide the most statistically significant improvement, repeating this until no further variables add to the explanatory power of the model.a b Dalgaard, Peter (2008). "Power and the computation of sample size". Introductory Statistics with R. Statistics and Computing. New York: Springer. pp.155–56. doi: 10.1007/978-0-387-79054-1_9. ISBN 978-0-387-79053-4. This step also became an invitation for governments, international organizations as well and NGOs to join together and organize activities designed to raise public awareness of the issue every year on this date. International Day For The Elimination of Violence Against Women: How You Can Help Neyman, J.; Pearson, E.S. (1933). "The testing of statistical hypotheses in relation to probabilities a priori". Mathematical Proceedings of the Cambridge Philosophical Society. 29 (4): 492–510. Bibcode: 1933PCPS...29..492N. doi: 10.1017/S030500410001152X. S2CID 119855116. a b c Wasserstein, Ronald L.; Lazar, Nicole A. (2016-04-02). "The ASA's Statement on p-Values: Context, Process, and Purpose". The American Statistician. 70 (2): 129–133. doi: 10.1080/00031305.2016.1154108. Johnson, Valen E. (October 9, 2013). "Revised standards for statistical evidence". Proceedings of the National Academy of Sciences. 110 (48): 19313–19317. Bibcode: 2013PNAS..11019313J. doi: 10.1073/pnas.1313476110. PMC 3845140. PMID 24218581.