A reappraisal of the role of spatial science and statistical inference in geography in Britain

This article emphasizes various ways by which the study of mechanisms can make quantitative research more useful for causal inference. It concentrates on three aspects of the role of mechanisms in causal and statistical inference: how an understanding of the mechanisms at work can improve statistical inference by guiding the specification of the statistical models to be estimated; how mechanisms can strengthen causal inferences by improving our understanding of why individuals do what they do; and how mechanism-based models can strengthen causal inferences by showing why, acting as they do, individuals bring about the social outcomes they do. There has been a surge of interest in mechanism-based explanations, in political science as well as in sociology. Most of this work has been vital and valuable in that it has sought to clarify the distinctiveness of the approach and to apply it empirically.

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Statistical Inference by Confidence Intervals: Issues of Interpretation and Utilization

Physical Therapy ◽

10.1093/ptj/79.2.186 ◽

1999 ◽

Vol 79 (2) ◽

pp. 186-195 ◽

Cited By ~ 69

Author(s):

Julius Sim ◽

Norma Reid

Keyword(s):

Hypothesis Testing ◽

Statistical Inference ◽

Hypothesis Test ◽

Statistical Significance ◽

Aggregated Data ◽

Additional Information ◽

Point Estimates ◽

Sample Statistic ◽

Meta Analyses

Abstract This article examines the role of the confidence interval (CI) in statistical inference and its advantages over conventional hypothesis testing, particularly when data are applied in the context of clinical practice. A CI provides a range of population values with which a sample statistic is consistent at a given level of confidence (usually 95%). Conventional hypothesis testing serves to either reject or retain a null hypothesis. A CI, while also functioning as a hypothesis test, provides additional information on the variability of an observed sample statistic (ie, its precision) and on its probable relationship to the value of this statistic in the population from which the sample was drawn (ie, its accuracy). Thus, the CI focuses attention on the magnitude and the probability of a treatment or other effect. It thereby assists in determining the clinical usefulness and importance of, as well as the statistical significance of, findings. The CI is appropriate for both parametric and nonparametric analyses and for both individual studies and aggregated data in meta-analyses. It is recommended that, when inferential statistical analysis is performed, CIs should accompany point estimates and conventional hypothesis tests wherever possible.

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Dealing with the replication crisis in psychological science: The contribution of Type M and Type S errors

10.31237/osf.io/w63h7 ◽

2019 ◽

Author(s):

Giulia Bertoldo

Keyword(s):

Statistical Inference ◽

Design Analysis ◽

Original Study ◽

Type I ◽

Retrospective Design ◽

Error Magnitude ◽

Replication Crisis ◽

Crisis In Psychology ◽

Type Ii Errors

The present work aims to analyze the replicability crisis in psychology with a focus on statistical inference. The main objective is to highlight the risks to beware when performing hypotheses tests in a Frequentist framework. In addition to the classic Type I and Type II errors, two other errors that are not commonly considered are Type M error (magnitude) and Type S error (sign), concerning the size and direction of the effects. The first chapter introduces Null Hypothesis Significance Testing (NHST), the prevalent approach to statistical inference in the social sciences, following a historical perspective and presenting also the approaches of the statisticians Ronald Fisher, Jerzy Neyman and Egon Pearson. The second chapter discusses the replicability crisis in psychology with an analysis of the origins, the factors that contributed to the crisis and solutions proposed for a change of direction. The third chapter analyzes the role of Type M and Type S errors in the replicability crisis. Studies with a high probability of committing these two types of errors could provide estimates of effects that are exaggerated and/or in the wrong direction. Two types of analysis are presented to examine these errors before conducting a study (prospective design analysis) or once the study has already been conducted (retrospective design analysis). The fourth chapter aims to link Type M and Type S errors with the decline effect, which is the observation that the magnitude of effects decreases with repeated replications. Although there may be multiple reasons behind the decline effect, a possible explanation is that the original study overestimated the effect. A case study illustrates how a retrospective design analysis of the original study can provide information on the probability of Type M and Type S errors and give support to the hypothesis of overestimation. The final chapter summarizes the contributions of Type M and Type S errors to the replication crisis and the role of a design analysis in planning studies and analyzing results.

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Some Comments on Teaching Biostatistics in Medical and Health Sciences

Methods of Information in Medicine ◽

10.1055/s-0038-1634765 ◽

1990 ◽

Vol 29 (01) ◽

pp. 41-43 ◽

Cited By ~ 4

Author(s):

H. Sahai

Keyword(s):

Statistical Inference ◽

Statistical Methods ◽

Medical Diagnosis ◽

Health Sciences ◽

Diagnosis And Prognosis ◽

Teachers And Students ◽

Health Sciences Students

AbstractThe role of statistical methods is now well recognized in health sciences since these disciplines are concerned with the study of communities or populations where the principles of sampling and statistical inference are clearly applicable. However, many medical and health sciences teachers and students have been slower to perceive the need for knowledge of biostatistical methods, even though all aspects of medical diagnosis and prognosis are governed by the laws of probability. Some of them are still skeptical about the value and importance of biostatistical principles to their fields and raise questions about the meaning, content, and nature of biostatistics and relevance of its teaching to health sciences disciplines. The purpose of this essay is to address some of these issues with the hope to invoke comments and responses from other biostatistics instructors who have encountered similar predicaments in their teaching and consulting roles to health sciences students and professionals.

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Giving Birth to Inferential Reasoning

Mathematics Teacher ◽

10.5951/mtlt.2019.0152 ◽

2020 ◽

Vol 113 (4) ◽

pp. 287-292

Author(s):

Ryan Seth Jones ◽

Zhigang Jia ◽

Joel Bezaire

Keyword(s):

Statistical Inference ◽

Real World ◽

Inferential Reasoning ◽

Building Models ◽

Variable Data ◽

Repeated Samples

Too often, statistical inference and probability are treated in schools like they are unrelated. In this paper, we describe how we supported students to learn about the role of probability in making inferences with variable data by building models of real world events and using them to simulate repeated samples.

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