File Name: inferential statistics estimation and hypothesis testing .zip
In psychology as elsewhere, the main statistical inference strategy to establish empirical effects is null-hypothesis significance testing NHST. The recent failure to replicate allegedly well-established NHST-results, however, implies that such results lack sufficient statistical power, and thus feature unacceptably high error-rates. Using data-simulation to estimate the error-rates of NHST-results, we advocate the research program strategy RPS as a superior methodology.
This book focuses on the meaning of statistical inference and estimation. Statistical inference is concerned with the problems of estimation of population parameters and testing hypotheses. Primarily aimed at undergraduate and postgraduate students of statistics, the book is also useful to professionals and researchers in statistical, medical, social and other disciplines. It discusses current methodological techniques used in statistics and related interdisciplinary areas. Every concept is supported with relevant research examples to help readers to find the most suitable application. The book will help readers to discover diverse perspectives of statistical theory followed by relevant worked-out examples. Keeping in mind the needs of readers, as well as constantly changing scenarios, the material is presented in an easy-to-understand form.
Sign in. Statistical inference is the process of making reasonable guesses about the population's distributio n and parameters given the observed data. Conducting hypothesis testing and constructing confidence interval are two examples of statistical inference. Hypothesis testing is the process of calculating the probability of observing sample statistics given the null hypothesis is true. With a similar process, we can calculate the confidence interval with a certain confidence level. A confidence interval is an interval estimation for a population parameter, which is point estimation plus and minus the critical value times sample standard error. This article will discuss the standard procedure of conducting hypothesis testing and estimating confidence intervals in the following different scenarios:.
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This article examines the role of the confidence interval CI in statistical inference and its advantages over conventional hypothesis testing, particularly when data are applied in the context of clinical practice. Conventional hypothesis testing serves to either reject or retain a null hypothesis. A CI, while also functioning as a hypothesis test, provides additional information on the variability of an observed sample statistic ie, its precision and on its probable relationship to the value of this statistic in the population from which the sample was drawn ie, its accuracy. Thus, the CI focuses attention on the magnitude and the probability of a treatment or other effect. It thereby assists in determining the clinical usefulness and importance of, as well as the statistical significance of, findings. The CI is appropriate for both parametric and nonparametric analyses and for both individual studies and aggregated data in meta-analyses.
Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population. In machine learning , the term inference is sometimes used instead to mean "make a prediction, by evaluating an already trained model";  in this context inferring properties of the model is referred to as training or learning rather than inference , and using a model for prediction is referred to as inference instead of prediction ; see also predictive inference. Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling. Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of first selecting a statistical model of the process that generates the data and second deducing propositions from the model.
Published on September 4, by Pritha Bhandari. Revised on March 2, While descriptive statistics summarize the characteristics of a data set, inferential statistics help you come to conclusions and make predictions based on your data.
On average, the estimator gives the correct value for the parameter. Consistent estimators may be biased, but the bias must become smaller as the sample size increases if the consistency property holds true.
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