The paper by Wichmann and Hill (2001) focuses on fitting psychometric functions, assessing goodness of fit, and providing confidence intervals for parameters. The authors describe a constrained maximum-likelihood method for parameter estimation and develop several goodness-of-fit tests. They address two specific issues: the influence of stimulus-independent errors (or "lapses") and the small sample sizes typically encountered in psychophysical data. Using Monte Carlo simulations, they demonstrate how these issues can lead to biases in parameter estimates and advocate the use of Monte Carlo resampling techniques over traditional $\chi^2$ methods. The paper is divided into two main sections: fitting psychometric functions and goodness of fit, each further subdivided into introduction and simulation results. The authors also discuss the importance of Bayesian priors in constraining parameter estimates and provide software to implement their methods.The paper by Wichmann and Hill (2001) focuses on fitting psychometric functions, assessing goodness of fit, and providing confidence intervals for parameters. The authors describe a constrained maximum-likelihood method for parameter estimation and develop several goodness-of-fit tests. They address two specific issues: the influence of stimulus-independent errors (or "lapses") and the small sample sizes typically encountered in psychophysical data. Using Monte Carlo simulations, they demonstrate how these issues can lead to biases in parameter estimates and advocate the use of Monte Carlo resampling techniques over traditional $\chi^2$ methods. The paper is divided into two main sections: fitting psychometric functions and goodness of fit, each further subdivided into introduction and simulation results. The authors also discuss the importance of Bayesian priors in constraining parameter estimates and provide software to implement their methods.