Despite being a standard tool for data analysis in many scientific fields, statistical testing has always been associated to criticism and controversies. Recently, the debate on the role of statistical testing in science has been particularly vivid and coupled with the debate on the lack of reproducibility of scientific results in many disciplines. We review some recent critics and purported consequences of statistical testing use and abuse and discuss proposed remedies, which entails either improving testing or abandoning it for alternative, more appropriate, strategies
Comments on Rodgers (2010a, 2010b) and Robinson and Levin (2010) are presented. Rodgers (2010a) init...
Formalization of verifying procedure is the most effective method of hypotheses’ assessment. Paper d...
Science can be described as a systematic attempt to extract reliable infor-mation about the world. T...
In spite of the widespread use of significance testing in empirical research, its interpretation and...
Many people regard the concept of hypothesis testing as fundamental to inferential statistics. Vario...
Many people regard the concept of hypothesis testing as fundamental to inferential statistics. Vario...
Statistical hypothesis testing is common in research, but a conventional understanding s...
Experimental studies are usually designed with specific expectations about the results in mind. Howe...
Abstract. Science can be described as a systematic attempt to extract reliable infor-mation about th...
One of the greatest machine learning prob-lems of today is an intractable number of new algorithms b...
We review the recent debate on the lack of reliability of scientific results and its connections to ...
Null Hypothesis Significance Testing (NHST) is reviewed in a historical context. The most vocal crit...
While the common procedure of statistical significance testing and its accompanying concept of p-val...
This symposium will introduce and discuss how scholars can improve upon statistical significance tes...
While the common procedure of statistical significance testing and its accompanying concept of p-val...
Comments on Rodgers (2010a, 2010b) and Robinson and Levin (2010) are presented. Rodgers (2010a) init...
Formalization of verifying procedure is the most effective method of hypotheses’ assessment. Paper d...
Science can be described as a systematic attempt to extract reliable infor-mation about the world. T...
In spite of the widespread use of significance testing in empirical research, its interpretation and...
Many people regard the concept of hypothesis testing as fundamental to inferential statistics. Vario...
Many people regard the concept of hypothesis testing as fundamental to inferential statistics. Vario...
Statistical hypothesis testing is common in research, but a conventional understanding s...
Experimental studies are usually designed with specific expectations about the results in mind. Howe...
Abstract. Science can be described as a systematic attempt to extract reliable infor-mation about th...
One of the greatest machine learning prob-lems of today is an intractable number of new algorithms b...
We review the recent debate on the lack of reliability of scientific results and its connections to ...
Null Hypothesis Significance Testing (NHST) is reviewed in a historical context. The most vocal crit...
While the common procedure of statistical significance testing and its accompanying concept of p-val...
This symposium will introduce and discuss how scholars can improve upon statistical significance tes...
While the common procedure of statistical significance testing and its accompanying concept of p-val...
Comments on Rodgers (2010a, 2010b) and Robinson and Levin (2010) are presented. Rodgers (2010a) init...
Formalization of verifying procedure is the most effective method of hypotheses’ assessment. Paper d...
Science can be described as a systematic attempt to extract reliable infor-mation about the world. T...