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3 Σεπ 2010 · Learn the basics of hypothesis testing, including null and alternative hypotheses, types of errors, test statistics, and examples. This presentation covers one-tail and two-tail tests for population means, proportions, and standard deviations.
15 Ιαν 2013 · This document discusses hypothesis testing, including: 1) The objectives are to formulate statistical hypotheses, discuss types of errors, establish decision rules, and choose appropriate tests. 2) Key symbols and concepts are defined, such as the null and alternative hypotheses, Type I and Type II errors, test statistics like z and t, means, ...
Learn how to use statistical tests to compare populations, measure effects, and draw conclusions from data. This presentation covers topics such as p-values, errors, degrees of freedom, t-tests, ANOVA, and post-hoc tests.
16 Δεκ 2013 · This document provides an overview of quantitative data analysis techniques for hypothesis testing, including types of errors, statistical power, and tests for single and multiple sample means.
Learn how to conduct hypothesis tests for proportions, means, and differences between means using examples from news and journals. See how to interpret p-values, test statistics, and levels of significance in various contexts.
This document provides an overview of statistical inference and hypothesis testing. It discusses key concepts like point estimation, confidence intervals, sample means, and hypothesis testing errors. Specific hypothesis tests covered include tests for a mean (using z-tests and t-tests) and tests for a proportion.
It discusses the key steps in hypothesis testing, including formulating the null and alternative hypotheses, determining the significance level, calculating the test statistic, defining the critical region, and interpreting the results. It also describes point and interval estimation.
