219. Sample Sizes for Hypothesis Testing with Leftcensored Lognormal Distributions

1999 ◽  
Author(s):  
J. Coble ◽  
P. Lees
Keyword(s):  
Hypothesis Testing ◽  
Sample Sizes ◽  
Lognormal Distributions

Technology Tips: A Fathom Activity for the Central Limit Theorem

2008 ◽  
Vol 102 (2) ◽  
pp. 151-153
Author(s):  
Todd O. Moyer ◽  
Edward Gambler
Keyword(s):  
Central Limit Theorem ◽  
Limit Theorem ◽  
Hypothesis Testing ◽  
Confidence Intervals ◽  
Central Limit ◽  
Skewed Distribution ◽  
Sampling Distribution ◽  
Sample Sizes ◽  
The Central Limit Theorem ◽  
Lecture Method

The central limit theorem, the basis for confidence intervals and hypothesis testing, is a critical theorem in statistics. Instructors can approach this topic through lecture or activity. In the lecture method, the instructor tells students about the central limit theorem. Typically, students are informed that a sampling distribution of means for even an obviously skewed distribution will approach normality as the sample sizes used approach 30. Consequently, students may be able to use the theorem, but they may not necessarily understand the theorem.

scholarly journals Hypothesis-Testing Improves the Predicted Reliability of Neuroscience Research

2019 ◽  
Author(s):  
Bradley E. Alger
Keyword(s):  
Hypothesis Testing ◽  
Statistical Tests ◽  
Laboratory Research ◽  
Experimental Sample ◽  
Test Results ◽  
Sample Sizes ◽  
Multiple Tests ◽  
Neuroscience Research ◽  
Statistical Problems ◽  
Adequate Power

AbstractCritics often cite statistical problems as prime contributors to the “reproducibility crisis” of science, expressing great concern about research that bases major conclusions on single p-valued statistical tests. The critics also argue that the predicted reliability of neuroscience research in particular is low because much of the work depends heavily on small experimental sample sizes and, hence, its statistical tests lack adequate “power.”It isn’t known how common the practice of basing major conclusions on single tests is in neuroscience or how the statistical criticisms affect the validity of conclusions drawn by laboratory research that evaluates hypotheses via multiple tests. I review a sample of neuroscience publications to estimate the prevalence and extensiveness of hypothesis-testing research. I then apply R.A. Fisher’s method for combining test results to show that the practice of testing multiple predictions of hypotheses increases the predicted reliability of neuroscience research.

Small Sample Asymptotics

1988 ◽  
Vol 13 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Michael A. Fligner
Keyword(s):  
Hypothesis Testing ◽  
Asymptotic Theory ◽  
Small Sample ◽  
Sample Sizes ◽  
Statistical Procedures

An approach for modifying the results of asymptotic theory to improve the performance of statistical procedures in small to moderate sample sizes is described in the context of hypothesis testing. The method is illustrated by a series of examples.

scholarly journals Confidence Intervals for the Ratio of Means of Two Independent Log-Normal Distributions

2021 ◽  
Vol 20 ◽  
pp. 45-52
Author(s):  
Lapasrada Singhasomboon ◽  
Wararit Panichkitkosolkul ◽  
Andrei Volodin
Keyword(s):  
Confidence Intervals ◽  
Coverage Probability ◽  
Real Life ◽  
Sample Sizes ◽  
Simple Method ◽  
Large Sample ◽  
Lognormal Distributions ◽  
Coverage Probabilities ◽  
Interval Width ◽  
Log Normal

In this paper, we investigate confidence intervals for the ratio of means of two independent lognormal distributions. The normal approximation (NA) approach was proposed. We compared the proposed with another approaches, the ML, GCI, and MOVER. The performance of these approaches were evaluated in terms of coverage probabilities and interval widths. The Simulation studies and results showed that the GCI and MOVER approaches performed similar in terms of the coverage probability and interval width for all sample sizes. The ML and NA approaches provided the coverage probability close to nominal level for large sample sizes. However, our proposed method provided the interval width shorter than other methods. Overall, our proposed is conceptually simple method. We recommend that our proposed approach is appropriate for large sample sizes because it is consistently performs well in terms of the coverage probability and the interval width is typically shorter than the other approaches. Finally, the proposed approaches are illustrated using a real-life example.

Ten categories of statistical errors: a guide for research in endocrinology and metabolism

2004 ◽  
Vol 286 (4) ◽  
pp. E495-E501 ◽  
Author(s):  
Tyson H. Holmes
Keyword(s):  
Hypothesis Testing ◽  
Statistical Error ◽  
Small Sample ◽  
Sample Sizes ◽  
Statistical Errors ◽  
Error Bias ◽  
Small Sample Sizes

A simple framework is introduced that defines ten categories of statistical errors on the basis of type of error, bias or imprecision, and source: sampling, measurement, estimation, hypothesis testing, and reporting. Each of these ten categories is illustrated with examples pertinent to research and publication in the disciplines of endocrinology and metabolism. Some suggested remedies are discussed, where appropriate. A review of recent issues of American Journal of Physiology: Endocrinology and Metabolism and of Endocrinology finds that very small sample sizes may be the most prevalent cause of statistical error in this literature.

Sign in / Sign up

Export Citation Format

Share Document