Search results
Results from the WOW.Com Content Network
The GAISE College Report begins by synthesizing the history and current understanding of introductory statistics courses and then lists goals for students based on statistical literacy. [13] Six recommendations for introductory statistics courses are given, namely: [14] Emphasize statistical thinking and literacy over other outcomes
OpenIntro Statistics is an open-source textbook for introductory statistics, written by David Diez, Christopher Barr, and Mine Çetinkaya-Rundel. [ 1 ] The textbook is available online as a free PDF, as LaTeX source and as a royalty-free paperback.
Statistics educators have cognitive and noncognitive goals for students. For example, former American Statistical Association (ASA) President Katherine Wallman defined statistical literacy as including the cognitive abilities of understanding and critically evaluating statistical results as well as appreciating the contributions statistical thinking can make.
Probability is used in mathematical statistics to study the sampling distributions of sample statistics and, more generally, the properties of statistical procedures. The use of any statistical method is valid when the system or population under consideration satisfies the assumptions of the method.
An introductory statistics class teaches hypothesis testing as a cookbook process. Hypothesis testing is also taught at the postgraduate level. Statisticians learn how to create good statistical test procedures (like z , Student's t , F and chi-squared).
A one-sample Student's t-test is a location test of whether the mean of a population has a value specified in a null hypothesis. In testing the null hypothesis that the population mean is equal to a specified value μ 0 , one uses the statistic
inferential statistics – the part of statistics that draws conclusions from data (using some model for the data): For example, inferential statistics involves selecting a model for the data, checking whether the data fulfill the conditions of a particular model, and with quantifying the involved uncertainty (e.g. using confidence intervals).
Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...