Parapsychology, the laboratory study of psychic phenomena, has had its history interwoven, with that of statistics. Many of the controversies in parapsychology have focused on statistical issues, and statistical models have played an integral role in the experimental work. Recently, parapsychologists have been using meta-analysis as a tool for synthesizing large bodies of work. This paper presents an overview of the use of statistics in parapsychology and offers a summary of the meta-analyses that have been conducted. It begins with some anecdotal information about the involvement of statistics and statisticians with the early history of parapsychology. Next, it is argued that most nonstatisticians do not appreciate the connection between power and "successful" replication of experimental effects. Returning to parapsychology, a particular experimental regime is examined by summarizing an extended debate over the interpretation of the results. A new set of experiments designed to resolve the debate is then reviewed. Finally, meta-analyses from several areas of parapsychology are summarized. It is concluded that the overall evidence indicates that there is an anomalous effect in need of an explanation.

For nonparametric regression, there are two popular methods for constructing kernel estimators, involving choosing weights either by direct kernel evaluation or by the convolution of the kernel with a histogram representing the data. There is an extensive literature concerning both of these estimators, but a comparatively small amount of thought has been given to the choice between them. The few papers that do treat both types of estimator tend to present only one side of the pertinent issues. The purpose of this paper is to present a balanced discussion, at an intuitive level, of the differences between the estimators, to allow users of nonparametric regression to rationally make this choice for themselves. While these estimators give very nearly the same performance in the case of a fixed, essentially equally spaced design, their performance is quite different when there are serious departures from equal spacing, or when the design points are randomly chosen. Each of the estimators has several important advantages and disadvantages, so the choice of "best" is a personal one, which should depend on the particular estimation setting at hand.