scholarly journals The Bayesian Inferential Paradigm in Archaeology

2021 ◽  
Author(s):  
Erik Otarola-Castillo ◽  
Meissa G Torquato ◽  
Caitlin E. Buck
Keyword(s):  
Bayesian Statistics ◽  
Null Hypothesis ◽  
Radiocarbon Dating ◽  
Relevant Literature ◽  
Significance Testing ◽  
Simple Explanation ◽  
Spatial Analyses ◽  
Null Hypothesis Significance Testing ◽  
Statistical Framework ◽  
Research Problems

Archaeologists often use data and quantitative statistical methods to evaluate their ideas. Although there are various statistical frameworks for decision-making in archaeology and science in general, in this chapter, we provide a simple explanation of Bayesian statistics. To contextualize the Bayesian statistical framework, we briefly compare it to the more widespread null hypothesis significance testing (NHST) approach. We also provide a simple example to illustrate how archaeologists use data and the Bayesian framework to compare hypotheses and evaluate their uncertainty. We then review how archaeologists have applied Bayesian statistics to solve research problems related to radiocarbon dating and chronology, lithic, ceramic, zooarchaeological, bioarchaeological, and spatial analyses. Because recent work has reviewed Bayesian applications in archaeology from the 1990s up to 2017, this work considers the relevant literature published since 2017.

Bayesian Statistics in Archaeology

2018 ◽  
Vol 47 (1) ◽  
pp. 435-453 ◽  
Author(s):  
Erik Otárola-Castillo ◽  
Melissa G. Torquato
Keyword(s):  
Bayesian Inference ◽  
Bayesian Statistics ◽  
Bayesian Methods ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Archaeological Research ◽  
Historical Overview ◽  
Spatial Analyses ◽  
Null Hypothesis Significance Testing ◽  
Statistical Framework

Null hypothesis significance testing (NHST) is the most common statistical framework used by scientists, including archaeologists. Owing to increasing dissatisfaction, however, Bayesian inference has become an alternative to these methods. In this article, we review the application of Bayesian statistics to archaeology. We begin with a simple example to demonstrate the differences in applying NHST and Bayesian inference to an archaeological problem. Next, we formally define NHST and Bayesian inference, provide a brief historical overview of their development, and discuss the advantages and limitations of each method. A review of Bayesian inference and archaeology follows, highlighting the applications of Bayesian methods to chronological, bioarchaeological, zooarchaeological, ceramic, lithic, and spatial analyses. We close by considering the future applications of Bayesian statistics to archaeological research.

Things We Still Haven’t Learned (So Far)

2015 ◽  
Vol 37 (4) ◽  
pp. 449-461 ◽  
Author(s):  
Andreas Ivarsson ◽  
Mark B. Andersen ◽  
Andreas Stenling ◽  
Urban Johnson ◽  
Magnus Lindwall
Keyword(s):  
Bayesian Statistics ◽  
Null Hypothesis ◽  
Historical Background ◽  
Effect Sizes ◽  
Significance Testing ◽  
Exercise Psychology ◽  
Null Hypothesis Significance Testing ◽  
The Past ◽  
The Common ◽  
Sport And Exercise

Null hypothesis significance testing (NHST) is like an immortal horse that some researchers have been trying to beat to death for over 50 years but without any success. In this article we discuss the flaws in NHST, the historical background in relation to both Fisher’s and Neyman and Pearson’s statistical ideas, the common misunderstandings of what p < 05 actually means, and the 2010 APA publication manual’s clear, but most often ignored, instructions to report effect sizes and to interpret what they all mean in the real world. In addition, we discuss how Bayesian statistics can be used to overcome some of the problems with NHST. We then analyze quantitative articles published over the past three years (2012–2014) in two top-rated sport and exercise psychology journals to determine whether we have learned what we should have learned decades ago about our use and meaningful interpretations of statistics.

scholarly journals Scientific Self-Correction: The Bayesian Way

2019 ◽  
Author(s):  
Felipe Romero ◽  
Jan Sprenger
Keyword(s):  
Bayesian Inference ◽  
Bayesian Statistics ◽  
Experimental Research ◽  
Null Hypothesis ◽  
Effect Sizes ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Scientific Disciplines ◽  
Replication Crisis ◽  
Reliable Knowledge

The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to self-correct its findings and to produce reliable knowledge. Amongst a variety of possible methodological, social, and statistical reforms to address the crisis, we focus on replacing null hypothesis significance testing (NHST) with Bayesian inference. On the basis of a simulation study for meta-analytic aggregation of effect sizes, we study the relative advantages of this Bayesian reform, and its interaction with widespread limitations in experimental research. Moving to Bayesian statistics will not solve the replication crisis single-handely, but would eliminate important sources of effect size overestimation for the conditions we study.

scholarly journals To P or Not to P: Backing Bayesian Statistics

2017 ◽  
Vol 157 (6) ◽  
pp. 915-918 ◽  
Author(s):  
Farrel J. Buchinsky ◽  
Neil K. Chadha
Keyword(s):  
Bayesian Statistics ◽  
Biomedical Research ◽  
Null Hypothesis ◽  
Study Data ◽  
Significance Testing ◽  
Posterior Probabilities ◽  
Null Hypothesis Significance Testing ◽  
P Values ◽  
Prior Probabilities

In biomedical research, it is imperative to differentiate chance variation from truth before we generalize what we see in a sample of subjects to the wider population. For decades, we have relied on null hypothesis significance testing, where we calculate P values for our data to decide whether to reject a null hypothesis. This methodology is subject to substantial misinterpretation and errant conclusions. Instead of working backward by calculating the probability of our data if the null hypothesis were true, Bayesian statistics allow us instead to work forward, calculating the probability of our hypothesis given the available data. This methodology gives us a mathematical means of incorporating our “prior probabilities” from previous study data (if any) to produce new “posterior probabilities.” Bayesian statistics tell us how confidently we should believe what we believe. It is time to embrace and encourage their use in our otolaryngology research.

scholarly journals A Primer on Bayesian Analysis for Experimental Psychopathologists

2017 ◽  
Vol 8 (2) ◽  
pp. 140-157 ◽  
Author(s):  
Angelos-Miltiadis Krypotos ◽  
Tessa F. Blanken ◽  
Inna Arnaudova ◽  
Dora Matzke ◽  
Tom Beckers
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Statistics ◽  
Present Article ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Data Set ◽  
Experimental Psychopathology ◽  
Continued Use ◽  
Main Message

The principal goals of experimental psychopathology (EPP) research are to offer insights into the pathogenic mechanisms of mental disorders and to provide a stable ground for the development of clinical interventions. The main message of the present article is that those goals are better served by the adoption of Bayesian statistics than by the continued use of null-hypothesis significance testing (NHST). In the first part of the article we list the main disadvantages of NHST and explain why those disadvantages limit the conclusions that can be drawn from EPP research. Next, we highlight the advantages of Bayesian statistics. To illustrate, we then pit NHST and Bayesian analysis against each other using an experimental data set from our lab. Finally, we discuss some challenges when adopting Bayesian statistics. We hope that the present article will encourage experimental psychopathologists to embrace Bayesian statistics, which could strengthen the conclusions drawn from EPP research.

scholarly journals A Frequentist Alternative to Significance Testing, p-Values, and Confidence Intervals

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 26 ◽  
Author(s):  
David Trafimow
Keyword(s):  
Present Article ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
A Priori ◽  
Significance Testing ◽  
Population Parameters ◽  
Null Hypothesis Significance Testing ◽  
P Values ◽  
Statistical Procedures ◽  
Major Section

There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone.

The Numbers Will Love You Back in Return—I Promise

2016 ◽  
Vol 11 (4) ◽  
pp. 551-554 ◽  
Author(s):  
Martin Buchheit
Keyword(s):  
Sample Size ◽  
Null Hypothesis ◽  
Clinical Medicine ◽  
Statistical Significance ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Sport Science ◽  
Size Dependent ◽  
Research Questions ◽  
Per Se

The first sport-science-oriented and comprehensive paper on magnitude-based inferences (MBI) was published 10 y ago in the first issue of this journal. While debate continues, MBI is today well established in sport science and in other fields, particularly clinical medicine, where practical/clinical significance often takes priority over statistical significance. In this commentary, some reasons why both academics and sport scientists should abandon null-hypothesis significance testing and embrace MBI are reviewed. Apparent limitations and future areas of research are also discussed. The following arguments are presented: P values and, in turn, study conclusions are sample-size dependent, irrespective of the size of the effect; significance does not inform on magnitude of effects, yet magnitude is what matters the most; MBI allows authors to be honest with their sample size and better acknowledge trivial effects; the examination of magnitudes per se helps provide better research questions; MBI can be applied to assess changes in individuals; MBI improves data visualization; and MBI is supported by spreadsheets freely available on the Internet. Finally, recommendations to define the smallest important effect and improve the presentation of standardized effects are presented.

Роль показника розміру ефекту в сучасних психологічних дослідженнях

2021 ◽  
Author(s):  
Валерій Боснюк
Keyword(s):  
Null Hypothesis ◽  
Significance Testing ◽  
P Value ◽  
Null Hypothesis Significance Testing

Для підтвердження результатів дослідження в психологічних наукових роботах протягом багатьох років використовується процедура перевірки значущості нульової гіпотези (загальноприйнята абревіатура NHST – Null Hypothesis Significance Testing) із застосуванням спеціальних статистичних критеріїв. При цьому здебільшого значення статистики «p» (p-value) розглядається як еквівалент важливості отриманих результатів і сили наукових доказів на користь практичного й теоретичного ефекту дослідження. Таке некоректне використання та інтерпретації p-value ставить під сумнів застосування статистики взагалі та загрожує розвитку психології як науки. Ототожнення статистичного висновку з науковим висновком, орієнтація виключно на новизну в наукових дослідженнях, ритуальна прихильність дослідників до рівня значущості 0,05, опора на статистичну категоричність «так/ні» під час прийняття рішення призводить до того, що психологія примножує тільки результати про наявність ефекту без врахування його величини, практичної цінності. Дана робота призначена для аналізу обмеженості p-value при інтерпретації результатів психологічних досліджень та переваг представлення інформації про розмір ефекту. Застосування розмірів ефекту дозволить здійснити перехід від дихотомічного мислення до оціночного, визначати цінність результатів незалежно від рівня статистичної значущості, приймати рішення більш раціонально та обґрунтовано. Обґрунтовується позиція, що автор наукової роботи при формулюванні висновків дослідження не повинен обмежуватися одним єдиним показником рівня статистичної значущості. Осмислені висновки повинні базуватися на розумному балансуванні p-value та інших не менш важливих параметрів, одним з яких виступає розмір ефекту. Ефект (відмінність, зв’язок, асоціація) може бути статистично значущим, а його практична (клінічна) цінність – незначною, тривіальною. «Статистично значущий» не означає «корисний», «важливий», «цінний», «значний». Тому звернення уваги психологів до питання аналізу виявленого розміру ефекту має стати обов’язковим при інтерпретації результатів дослідження.

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