Difference between revisions of "Introduction"

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==Ab ovo<ref>Latin for 'since the very beginning'</ref>==
==Ab ovo<ref>Latin for 'since the very beginning'</ref>==


Before delving into the analysis of Masticationpedia, we must first introduce some preliminary considerations, particularly regarding two fundamental dimensions—social, scientific, and clinical—that characterize both the present era and the one immediately preceding it.
Before delving into the analysis of Masticationpedia, we must first introduce some preliminary considerations, particularly regarding two fundamental dimensions—social and scientific-clinical aspect—that characterize both the present era and the one immediately preceding it.


===The phases of paradigm change according to Thomas Kuhn===
===The phases of paradigm change according to Thomas Kuhn===
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*'''P-value''': In medicine, for example, we rely on statistical inference to confirm experimental results, specifically the P-value, a "significance test" that assesses data validity. Yet, even this entrenched concept is now being challenged. A recent study highlighted a campaign in the journal "Nature" against the use of the P-value.<ref>{{cita libro  
*'''P-value''': In medicine, for example, we rely on statistical inference to confirm experimental results, specifically the {{Tooltip|P-value|2=The p-value represents the probability that observed results are due to chance, assuming the null hypothesis \( H_0 \) is true. It should not be used as a binary criterion (e.g., \( p < 0.05 \)) for scientific decisions, as values near the threshold require additional verification, such as cross-validation. *p-hacking* (repeating tests to achieve significance) increases false positives. Rigorous experimental design and transparency about all tests conducted can mitigate this risk. Type I error increases with multiple tests: for \( N \) independent tests at threshold \( \alpha \), the Family-Wise Error Rate (FWER) is \( FWER = 1 - (1 - \alpha)^N \). Bonferroni correction divides the threshold by the number of tests, \( p < \frac{\alpha}{N} \), but can increase false negatives. The False Discovery Rate (FDR) by Benjamini-Hochberg is less conservative, allowing more true discoveries with an acceptable proportion of false positives. The Bayesian approach uses prior knowledge to balance prior and data with a posterior distribution, offering a valid alternative to the p-value. To combine p-values from multiple studies, meta-analysis uses methods like Fisher's: \( \chi^2 = -2 \sum \ln(p_i) \). In summary, the p-value remains useful when contextualized and integrated with other measures, such as confidence intervals and Bayesian approaches.}}, a "significance test" that assesses data validity. Yet, even this entrenched concept is now being challenged. A recent study highlighted a campaign in the journal "Nature" against the use of the P-value.<ref>{{cita libro  
  | autore = Amrhein V
  | autore = Amrhein V
  | autore2 = Greenland S
  | autore2 = Greenland S
Editor, Editors, USER, admin, Bureaucrats, Check users, dev, editor, founder, Interface administrators, oversight, Suppressors, Administrators, translator
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