scholarly journals The Practical Alternative to the p Value Is the Correctly Used p Value

2021 ◽  
pp. 174569162095801
Author(s):  
Daniël Lakens
Keyword(s):  
Null Hypothesis ◽  
Scientific Literature ◽  
Scientific Research ◽  
P Value ◽  
Evidence Based ◽  
Statistical Software ◽  
P Values ◽  
Statistical Inferences ◽  
Equivalence Tests

Because of the strong overreliance on p values in the scientific literature, some researchers have argued that we need to move beyond p values and embrace practical alternatives. When proposing alternatives to p values statisticians often commit the “statistician’s fallacy,” whereby they declare which statistic researchers really “want to know.” Instead of telling researchers what they want to know, statisticians should teach researchers which questions they can ask. In some situations, the answer to the question they are most interested in will be the p value. As long as null-hypothesis tests have been criticized, researchers have suggested including minimum-effect tests and equivalence tests in our statistical toolbox, and these tests have the potential to greatly improve the questions researchers ask. If anyone believes p values affect the quality of scientific research, preventing the misinterpretation of p values by developing better evidence-based education and user-centered statistical software should be a top priority. Polarized discussions about which statistic scientists should use has distracted us from examining more important questions, such as asking researchers what they want to know when they conduct scientific research. Before we can improve our statistical inferences, we need to improve our statistical questions.

scholarly journals The practical alternative to the p-value is the correctly used p-value

2019 ◽  
Author(s):  
Daniel Lakens
Keyword(s):  
Null Hypothesis ◽  
Scientific Literature ◽  
Scientific Research ◽  
P Value ◽  
Evidence Based ◽  
Hypothesis Tests ◽  
Statistical Software ◽  
P Values ◽  
Statistical Inferences ◽  
Equivalence Tests

Due to the strong overreliance on p-values in the scientific literature some researchers have argued that p-values should be abandoned or banned, and that we need to move beyond p-values and embrace practical alternatives. When proposing alternatives to p-values statisticians often commit the ‘Statistician’s Fallacy’, where they declare which statistic researchers really ‘want to know’. Instead of telling researchers what they want to know, statisticians should teach researchers which questions they can ask. In some situations, the answer to the question they are most interested in will be the p-value. As long as null-hypothesis tests have been criticized, researchers have suggested to include minimum-effect tests and equivalence tests in our statistical toolbox, and these tests (even though they return p-values) have the potential to greatly improve the questions researchers ask. It is clear there is room for improvement in how we teach p-values. If anyone really believes p-values are an important cause of problems in science, preventing the misinterpretation of p-values by developing better evidence-based education and user-centered statistical software should be a top priority. Telling researchers which statistic they should use has distracted us from examining more important questions, such as asking researchers what they want to know when they do scientific research. Before we can improve our statistical inferences, we need to improve our statistical questions.

scholarly journals PENERAPAN PRAKTIK KEPERAWATAN BERBASIS BUKTI PURSED LIP BREATHING PADA PASIEN DENGAN PENYAKIT PARU OBSTRUKTIF KRONIK DI RUANG RSU PUSAT PERSAHABATAN JAKARTA

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Seven Sitorus
Keyword(s):  
Quality Of Care ◽  
Pulmonary Disease ◽  
Significant Influence ◽  
Exercise Tolerance ◽  
P Value ◽  
Evidence Based ◽  
Copd Exacerbation ◽  
Before And After ◽  
Evidence Based Nursing

Background: Chronic Obstruction Pulmonary Disease (COPD) is disease characterized by obstruction air flow in the breath not wholly reversible. One treatment can be done on improving exercise tolerance is exercise respiration as pursed lip breathing ( PLB ). Purse lip breathing is a techniques of breathing carried out to expelling air by creating power through  in move closer /pursed lips. Purpose: provide an illustration of the application of the practice of evidence based nursing of pursed lip breathing in patients COPD in RSUP Persahabatan Jakarta. Method:  the implementation of the practice of evidence based nursing pursed lip breathing is applied to 12 people sample ( 10 men and 2 women ) diagnosed with COPD exacerbation. Result: the majority of sex respondents is man as many as 10 ( 83,3 % ) persons and women as many as 2 ( 16,7 % ) a person .mean the age of respondents is 61,5 years ± 10.4 .mean the value of PEF ( Peak Expiratory Flow ), the value of the saturation oxygen , the value of respiratori rate before the intervention in a consecutive manner is 131.6 ±  44.6; 92.1 ± 2.44; 31.5 ±  2 . While value after the intervention is 175.0 ±  60.0; 97,1 ± 1.6; 22,6 ± 1.7 with P value = 0.001, α = 0.05. Conclusions: there are significant influence the application of pursed lip breathing between before and after the intervention in patients COPD. Advice: Intervention evidence based nursing can be applied to all patients COPD so reached the quality of care of nursing based on research

"MULTIPLE-COMPARISONS" PROBLEM

PEDIATRICS ◽  
1989 ◽  
Vol 84 (6) ◽  
pp. A30-A30
Author(s):  
Student
Keyword(s):  
Null Hypothesis ◽  
Multiple Comparisons ◽  
P Value ◽  
Expected Number ◽  
P Values ◽  
A Value ◽  
True Null Hypotheses ◽  
The Individual

Often investigators report many P values in the same study. The expected number of P values smaller than 0.05 is 1 in 20 tests of true null hypotheses; therefore the probability that at least one P value will be smaller than 0.05 increases with the number of tests, even when the null hypothesis is correct for each test. This increase is known as the "multiple-comparisons" problem...One reasonable way to correct for multiplicity is simply to multiply the P value by the number of tests. Thus, with five tests, an orignal 0.05 level for each is increased, perhaps to a value as high as 0.25 for the set. To achieve a level of not more than 0.05 for the set, we need to choose a level of 0.05/5 = 0.01 for the individual tests. This adjustment is conservative. We know only that the probability does not exceed 0.05 for the set.

Scientific Papers and Patents on Substances with Unproven Effects

2019 ◽  
Vol 13 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Sergei V. Jargin
Keyword(s):  
Medical Research ◽  
Dietary Supplements ◽  
Scientific Literature ◽  
Evidence Based ◽  
Scientific Papers ◽  
Constructive Discussion

It is evident from reviewing scientific literature that the quality of argumentation in some areas of medical research has deteriorated during the last decades. Publication of a series of questionable reliability has continued without making references to the published criticism; examples are discussed in this review. Another tendency is that drugs without proven efficiency are advertised, corresponding products patented and marketed as evidence-based medications. Professional publications are required to register drugs and dietary supplements to obtain permissions for the practical use; and such papers appeared, sometimes being of questionable reliability. Several examples are discussed in this review when substances without proven effects were patented and introduced into practice being supported by publications of questionable reliability. Some of the topics are not entirely clear; and the arguments provided here can induce a constructive discussion.

scholarly journals The frequent insignificance of a “significant” P-value

2021 ◽  
Author(s):  
David McGiffin ◽  
Geoff Cumming ◽  
Paul Myles
Keyword(s):  
Diagnostic Tests ◽  
Null Hypothesis ◽  
Open Science ◽  
Significance Testing ◽  
P Value ◽  
Conditional Probabilities ◽  
Null Hypothesis Significance Testing ◽  
P Values ◽  
Science Practices ◽  
Strength Of Evidence

Null hypothesis significance testing (NHST) and p-values are widespread in the cardiac surgical literature but are frequently misunderstood and misused. The purpose of the review is to discuss major disadvantages of p-values and suggest alternatives. We describe diagnostic tests, the prosecutor’s fallacy in the courtroom, and NHST, which involve inter-related conditional probabilities, to help clarify the meaning of p-values, and discuss the enormous sampling variability, or unreliability, of p-values. Finally, we use a cardiac surgical database and simulations to explore further issues involving p-values. In clinical studies, p-values provide a poor summary of the observed treatment effect, whereas the three- number summary provided by effect estimates and confidence intervals is more informative and minimises over-interpretation of a “significant” result. P-values are an unreliable measure of strength of evidence; if used at all they give only, at best, a very rough guide to decision making. Researchers should adopt Open Science practices to improve the trustworthiness of research and, where possible, use estimation (three-number summaries) or other better techniques.

Epidemiology 2: Study Design

2010 ◽  
pp. 47-58
Author(s):  
Muktar H. Aliyu
Keyword(s):  
Study Design ◽  
Scientific Evidence ◽  
Epidemiologic Study ◽  
Scientific Research ◽  
Time Frame ◽  
Epidemiologic Studies ◽  
Evidence Based ◽  
Study Designs ◽  
Based Medicine

The usefulness of evidence arising from scientific research is influenced by several factors, and foremost among these factors is the design of the epidemiologic study from which the findings are drawn. In evidence-based medicine, the quality of scientific evidence is often graded on the base of the type of study design and includes appraisal of methods by which studies of exposure and outcomes are planned and implemented. Several factors must be considered when designing a scientific study, including the hypothesis being tested, study cost, time frame, subject characteristics, choice of variables or measurements, and ethical concerns. In this chapter, the different types of study designs commonly encountered in clinical research, common measures of morbidity and mortality in epidemiology, and errors (random and systematic) that may threaten conclusions derived from inferences arising from epidemiologic studies are discussed.

scholarly journals Falacias sobre el valor p compartidas por profesores y estudiantes universitarios

2017 ◽  
Vol 16 (3) ◽  
pp. 1
Author(s):  
Laura Badenes-Ribera ◽  
Dolores Frias-Navarro
Keyword(s):  
College Students ◽  
Statistical Significance ◽  
Psychological Research ◽  
Practical Significance ◽  
P Value ◽  
P Values ◽  
Statistical Education ◽  
The Mean ◽  
Estudiantes Universitarios

Resumen La “Práctica Basada en la Evidencia” exige que los profesionales valoren de forma crítica los resultados de las investigaciones psicológicas. Sin embargo, las interpretaciones incorrectas de los valores p de probabilidad son abundantes y repetitivas. Estas interpretaciones incorrectas afectan a las decisiones profesionales y ponen en riesgo la calidad de las intervenciones y la acumulación de un conocimiento científico válido. Identificar el tipo de falacia que subyace a las decisiones estadísticas es fundamental para abordar y planificar estrategias de educación estadística dirigidas a intervenir sobre las interpretaciones incorrectas. En consecuencia, el objetivo de este estudio es analizar la interpretación del valor p en estudiantes y profesores universitarios de Psicología. La muestra estuvo formada por 161 participantes (43 profesores y 118 estudiantes). La antigüedad media como profesor fue de 16.7 años (DT = 10.07). La edad media de los estudiantes fue de 21.59 (DT = 1.3). Los hallazgos sugieren que los estudiantes y profesores universitarios no conocen la interpretación correcta del valor p. La falacia de la probabilidad inversa presenta mayores problemas de comprensión. Además, se confunde la significación estadística y la significación práctica o clínica. Estos resultados destacan la necesidad de la educación estadística y re-educación estadística. Abstract The "Evidence Based Practice" requires professionals to critically assess the results of psychological research. However, incorrect interpretations of p values of probability are abundant and repetitive. These misconceptions affect professional decisions and compromise the quality of interventions and the accumulation of a valid scientific knowledge. Identifying the types of fallacies that underlying statistical decisions is fundamental for approaching and planning statistical education strategies designed to intervene in incorrect interpretations. Therefore, the aim of this study is to analyze the interpretation of p value among college students of psychology and academic psychologist. The sample was composed of 161 participants (43 academic and 118 students). The mean number of years as academic was 16.7 (SD = 10.07). The mean age of college students was 21.59 years (SD = 1.3). The findings suggest that college students and academic do not know the correct interpretation of p values. The fallacy of the inverse probability presents major problems of comprehension. In addition, statistical significance and practical significance or clinical are confused. There is a need for statistical education and statistical re-education.

scholarly journals ERRORES DE INTERPRETACIÓN DE LOS VALORES P ENTRE PSICÓLOGOS PROFESIONALES ESPAÑOLES: UN ESTUDIO EXPLORATORIO

2017 ◽  
Vol 2 (1) ◽  
pp. 551
Author(s):  
Laura Badenes-Ribera ◽  
Dolores Frias-Navarro ◽  
Amparo Bonilla-Campos
Keyword(s):  
Professional Practice ◽  
Design Methodology ◽  
Practical Significance ◽  
P Value ◽  
Psychological Interventions ◽  
P Values ◽  
Know How ◽  
Professional Psychologists ◽  
Inference Tests

Abstract.ERRORS OF INTERPRETATION OF P VALUES AMONG SPANISH PROFESSIONAL PSYCHOLOGISTS: AN EXPLORATORY STUDYThis paper analyzes the misinterpretatons of the p-value made by Spanish practitioner psychologists given that lack of knowledge and understanding of p value might affect the professionals’ decisions and jeopardize the quality of psychological interventions. We carried out a survey on 77 practitioner psychologists from Spain (68.8% women, mean age of 41.44 years, SD = 9.42). Our findings are consistent with previous research and suggest that many participants did not know how to correctly interpret p values. More than a third of them believed that p-value indicate the clinical or practical significance of the findings. These results highlight the importance of the training in statistical concepts, research design methodology, and statistical inference tests in order to improve professional practice among practitioner psychologists.Keywords: p-value, miconceptions, effect size, fallacies.Resumen.Este artículo analiza los errores de interpretación del valor p cometidos por los psicólogos profesionales españoles dado que la falta de conocimiento y comprensión del valor de p puede afectar las decisiones de los profesionales y poner en peligro la calidad de las intervenciones psicológicas. Se realizó una encuesta a 77 psicólogos profesionales españoles (68,8% mujeres, con una edad media de 41.44 años, DT = 9,42). Nuestros hallazgos son consistentes con previas investigaciones y sugieren que muchos participantes no saben cómo interpretar correctamente los valores de p. Más de un tercio de ellos creyó que el valor p indica la importancia clínica o práctica de los hallazgos. Estos resultados destacan la importancia de la formación en conceptos estadísticos, metodología de diseño de investigación y pruebas de inferencia estadística con el fin de mejorar la práctica profesional entre los psicólogos profesionales.Palabras clave: valores de p, concepciones erróneas, tamaño del efecto, falacias.

scholarly journals A Primer on p-Value Thresholds and α-Levels – Two Different Kettles of Fish

2021 ◽  
Vol 70 (2) ◽  
pp. 123-133
Author(s):  
Norbert Hirschauer ◽  
Sven Grüner ◽  
Oliver Mußhoff ◽  
Claudia Becker
Keyword(s):  
Hypothesis Testing ◽  
Statistical Inference ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Future Research ◽  
P Value ◽  
Null Hypothesis Significance Testing ◽  
Realistic Assessment

It has often been noted that the “null-hypothesis-significance-testing” (NHST) framework is an inconsistent hybrid of Neyman-Pearson’s “hypothesis testing” and Fisher’s “significance testing” that almost inevitably causes misinterpretations. To facilitate a realistic assessment of the potential and the limits of statistical inference, we briefly recall widespread inferential errors and outline the two original approaches of these famous statisticians. Based on the understanding of their irreconcilable perspectives, we propose “going back to the roots” and using the initial evidence in the data in terms of the size and the uncertainty of the estimate for the purpose of statistical inference. Finally, we make six propositions that hopefully contribute to improving the quality of inferences in future research.

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