Slowly but surely, statistical practices in the empirical sciences are undergoing a complete makeover. Researchers in empirical software engineering, where too statistics is an essential tool, must become familiar with these new practices to ensure rigor of their research methods and soundness of their research results
The emergence in the past years of Bayesian analysis in many methodological and applied fields as th...
This chapter introduces the conceptual basis of the objective Bayesian approach to experimental data...
The Bayesian approach to quantifying, analysing and reducing uncertainty in the application of compl...
IEEE Statistics comes in two main flavors: frequentist and Bayesian. For historical and technical re...
Statistical analysis is the tool of choice to turn data into information and then information into e...
Software engineering research is maturing and papers increasingly support their arguments with empir...
\ua9 2019 Elsevier Inc. Software engineering research is evolving and papers are increasingly based ...
Systematic literature reviews in software engineering are necessary to synthesize evidence from mult...
There has been rapid improvement in the ability to construct software systems, firstly by developing...
Program analysis techniques are often used to deduce and infer targeted characteristics of software ...
Constructing an accurate effort prediction model is a challenge in Software Engineering. This paper ...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
Given the reproducibility crisis (or replication crisis), more psychologists and social-cultural sci...
Current statistical methods for facilitating data-driven decision making are too computationally int...
Frequentist statistical methods, such as hypothesis testing, are standard practices in studies that ...
The emergence in the past years of Bayesian analysis in many methodological and applied fields as th...
This chapter introduces the conceptual basis of the objective Bayesian approach to experimental data...
The Bayesian approach to quantifying, analysing and reducing uncertainty in the application of compl...
IEEE Statistics comes in two main flavors: frequentist and Bayesian. For historical and technical re...
Statistical analysis is the tool of choice to turn data into information and then information into e...
Software engineering research is maturing and papers increasingly support their arguments with empir...
\ua9 2019 Elsevier Inc. Software engineering research is evolving and papers are increasingly based ...
Systematic literature reviews in software engineering are necessary to synthesize evidence from mult...
There has been rapid improvement in the ability to construct software systems, firstly by developing...
Program analysis techniques are often used to deduce and infer targeted characteristics of software ...
Constructing an accurate effort prediction model is a challenge in Software Engineering. This paper ...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
Given the reproducibility crisis (or replication crisis), more psychologists and social-cultural sci...
Current statistical methods for facilitating data-driven decision making are too computationally int...
Frequentist statistical methods, such as hypothesis testing, are standard practices in studies that ...
The emergence in the past years of Bayesian analysis in many methodological and applied fields as th...
This chapter introduces the conceptual basis of the objective Bayesian approach to experimental data...
The Bayesian approach to quantifying, analysing and reducing uncertainty in the application of compl...
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