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Introduction to biostatistics: Part 5, Statistical inference techniques for hypothesis testing with nonparametric data.
Specific statistical tests are used when the null hypothesis (H0) is to be tested using nonparametric nominal or ordinal data. With nominal data, experimental results are expressed by proportions or frequencies. Chi-square or related tests (the Fisher's exact test or the rows by columns test) are appropriate for testing H0 with nominal data. Ordinal data permit arrangement of statistical results by rank. Rank-order tests used to test H0 with ordinal data include the Mann-Whitney U, Kolmogorov-Smirnov, Wilcoxon, Kruskal-Wallis, and Friedman tests. The Kruskal-Wallis and Friedman tests permit multiple intergroup comparisons. Other rank-order tests permit only single intergroup comparisons. Specific details to guide the researcher in the proper selection of these tests are presented.
Statistical inference on spontaneous neuronal discharge patterns. I. Single neuron.
A statistical analysis was performed on extracellularly recorded spike trains of spontaneously active mesencephalic reticular neurons of rats. Only stationary records were used for detailed examination. The moments of interspike intervals were computed, hypothesis of renewal process and its specific forms was tested. Implications for statistical methodology are considered on the basis of the results. The main emphasis is laid on the connection between experimental results and stochastic neuronal models.
Clinical biostatistics. LV. The t test and the basic ethos of parametric statistical inference (Part I).
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[SYSTEMATIC OUTLINE OF ERRORS OF STATISTICAL INFERENCE].
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Statistical inferences for a twin correlation with multinomial outcomes.
Current methods for statistical analysis of twin studies focus on continuous and dichotomous data, while only limited methodology exists for analysing multinomial data. As a consequence, investigators are often tempted to collapse multinomial data into two categories simply to facilitate the analysis. We address this problem by developing and evaluating two approaches to the assessment of twin correlation for an outcome variable having more than two nominal categories. One method developed is an extension of the goodness-of-fit approach, while the other method is based on large sample normal theory. Procedures for confidence interval construction are developed and compared using Monte Carlo simulation. The results show that either method may be safely used for confidence interval construction provided the number of twin pairs is large (> or =100) but that in smaller sample sizes the goodness-of-fit procedure is to be preferred on the grounds of validity. Other inference problems are also discussed, including point estimation, hypothesis testing and sample size estimation. An example is included.
Independence and statistical inference in clinical trial designs: a tutorial review.
The requirements for statistical approaches to the design, analysis, and interpretation of experimental data are now accepted by the scientific community. This is of particular importance in medical studies where public health consequences are of concern. Investigators in the clinical sciences should be cognizant of statistical principles in general, but should always be wary of the pursuing their own analyses and engage statisticians for data analysis whenever possible. Examples of circumstances that require statistical evaluation not found in textbooks and not always obvious to the lay person are pervasive. Incorrect statistical evaluation and analyses in such situations will result in erroneous and potentially serious misleading interpretation of clinical data. Although a statistician may not be responsible for any misinterpretations in such unfortunate circumstances, the quote often cited about statisticians and "damned liars" may appear to be more truth than fable. This article is a tutorial review and describes a common misuse of clinical data resulting in an apparently large sample size derived from a small number of patients. This mistake is a consequence of ignoring the dependency of results, treating multiple observations from a single patient as independent observations.
Statistical inference and the design of clinical trials.
According to the likelihood principle of statistics, a decision to stop or otherwise alter a clinical trial can be made on the basis of accumulating information without losing the ability to draw inferences from the results of the trial. In particular, balanced, randomized designs are not necessary. The probability that a particular treatment is the best among those in the trial can be calculated after each patient response, and may suggest that the treatment should be used predominantly in the next stage of the trial. Doing so results in more effective treatment of the patients in the trial while sacrifacing some of the information on the other treatments. Not doing so results in equal information on the treatments but sacrifices effective treatment. There are many trials in which compromise is possible between these antagonistic consequences.
Clinical biostatistics. LVI. The t test and the basic ethos of parametric statistical inference (conclusion).
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Commentary: Bootstrapping simplifies appreciation of statistical inferences.
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Statistical inference for individual organism research: mixed blessing or curse?
Descriptive and inferential statistics are described as judgemental aids, stimuli to which the scientist can more easily react than to his raw experimental results. The increasing emphasis on the significance test as the main judgemental aid utilized in experimental psychology is credited with several harmful effects on experimental practice. The area known as "the experimental analysis of behavior" has so far escaped most of these harmful effects, but now we see an increased interest in the development of appropriate significance tests for individual organism research. This interest is based on the view that it is not possible to effect adequate levels of experimental control with much human applied research, and that in such cases a significance test would be quite valuable as a judgemental aid, both of which points are considered to be essentially incorrect, and if accepted, potentially harmful.
Scientific vs. statistical inference: the problem of multiple contrasts in clinical research.
The problem of type 1 error rates in clinical medicine research is discussed and a new measure of evaluating the incidences of type 1 error is presented. Three measures, including the experimentwise error rate, the error rate per experiment, and the percentage error rate are used to compute the type 1 error in a sample of clinical research. The results suggest the presence of a substantial incidence of type 1 error in the sampled research. The implications for statistical and scientific inferences are briefly discussed and some possible solutions explored.
Statistical inference for infectious diseases. Risk-specific household and community transmission parameters.
A statistical model is presented for the analysis of infectious disease data from family studies in the community. The model partitions the sources of infection into those from within the household and those from the community at large. The parameters reflecting these sources of infection are estimated as functions of the risk factors. This new model is used to overcome problems associated with the lack of independence of observations in infectious disease data and negative confounding due to the association of unmeasured exposures and immunity. An example of how this new statistical model is used to provide a clearer and less confounded description of risk factor effects is presented for data from influenza A(H3N2) epidemic seasons in the Tecumseh Respiratory Illness Study. The risk factors examined are age and pre-epidemic season antibody level as measured by the hemagglutination-inhibition test, while the outcome is the infection rate. A standard analysis of the data indicates that the efficacy of protective antibodies is 70% in children and only 47% in adults. However, such an efficacy measurement is negatively confounded by past exposure which is age dependent. By means of the model, the true, unconfounded, efficacy of protective antibodies is shown to be 90% in both adults and children.
Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments.
We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.
Statistical inference as applied to bioavailability data--a guide for the practicing pharmacist.
Descriptive statistics and statistical tests used in reporting bioavailability data are reviewed in order to give the practicing pharmacist the fundamental knowledge needed to evaluate such data. Using acetaminophen bioavailability data as a model the following concepts are explained: mean, median, mode, standard deviation, range, 95% confidence limit, Student's t-test, analysis of variance, and the Wilcoxon rank sum test.
A note on an unexpected anchoring bias in intuitive statistical inference.
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Re: "effects of misclassification on statistical inferences in epidemiology".
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Letter: Normal chimpanzee blood values derived from statistical inference formulae.
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