The Merits of Confidence Intervals Relative to Hypothesis Testing

1992 ◽  
Vol 13 (9) ◽  
pp. 553-555 ◽  
Author(s):  
Leon F. Burmeister ◽  
David Bimbaum ◽  
Samuel B. Sheps
Keyword(s):  
Hypothesis Testing ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
Finite Population ◽  
Extreme Values ◽  
Statistical Tests ◽  
Point Estimate ◽  
Distributed Data ◽  
Average Difference ◽  
Average Value

A variety of statistical tests of a null hypothesis commonly are used in biomedical studies. While these tests are the mainstay for justifying inferences drawn from data, they have important limitations. This report discusses the relative merits of two different approaches to data analysis and display, and recommends the use of confidence intervals rather than classic hypothesis testing.Formulae for a confidence interval surrounding the point estimate of an average value take the form: d= ±zσ/√n, where “d” represents the average difference between central and extreme values, “z” is derived from the density function of a known distribution, and “a/-∨n” represents the magnitude of sampling variability. Transposition of terms yields the familiar formula for hypothesis testing of normally distributed data (without applying the finite population correction factor): z = d/(σ/√n).

Interval estimation and inference

2019 ◽  
pp. 111-138
Author(s):  
M. D. Edge
Keyword(s):  
Standard Deviation ◽  
Hypothesis Testing ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
Alternative Hypothesis ◽  
Interval Estimation ◽  
Research Literature ◽  
Significance Level ◽  
Specific Hypothesis ◽  
True Value

Interval estimation is the attempt to define intervals that quantify the degree of uncertainty in an estimate. The standard deviation of an estimate is called a standard error. Confidence intervals are designed to cover the true value of an estimand with a specified probability. Hypothesis testing is the attempt to assess the degree of evidence for or against a specific hypothesis. One tool for frequentist hypothesis testing is the p value, or the probability that if the null hypothesis is in fact true, the data would depart as extremely or more extremely from expectations under the null hypothesis than they were observed to do. In Neyman–Pearson hypothesis testing, the null hypothesis is rejected if p is less than a pre-specified value, often chosen to be 0.05. A test’s power function gives the probability that the null hypothesis is rejected given the significance level γ‎, a sample size n, and a specified alternative hypothesis. This chapter discusses some limitations of hypothesis testing as commonly practiced in the research literature.

scholarly journals Hypothesis Testing, p Values, Confidence Intervals, Measures of Effect Size, and Bayesian Methods in Light of Modern Robust Techniques

2016 ◽  
Vol 77 (4) ◽  
pp. 673-689 ◽  
Author(s):  
Rand R. Wilcox ◽  
Sarfaraz Serang
Keyword(s):  
Hypothesis Testing ◽  
Confidence Intervals ◽  
Bayesian Methods ◽  
Effect Size ◽  
Null Hypothesis ◽  
P Values ◽  
Null Hypothesis Testing ◽  
Robust Techniques ◽  
Alternative Techniques ◽  
Robust Statistical Methods

The article provides perspectives on p values, null hypothesis testing, and alternative techniques in light of modern robust statistical methods. Null hypothesis testing and p values can provide useful information provided they are interpreted in a sound manner, which includes taking into account insights and advances that have occurred during the past 50 years. There are, of course, limitations to what null hypothesis testing and p values reveal about data. But modern advances make it clear that there are serious limitations and concerns associated with conventional confidence intervals, standard Bayesian methods, and commonly used measures of effect size. Many of these concerns can be addressed using modern robust methods.

THE EFFECT OF COMIC STRIPS ON SECONDARY EFL STUDENTS’ READING COMPREHENSION AND WRITING ABILITY

2018 ◽  
Vol 2 (2) ◽  
pp. 43-57
Author(s):  
M. Ridhwan ◽  
Muhammad Taufik Ihsan ◽  
Naskah Naskah
Keyword(s):  
Reading Comprehension ◽  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Test Group ◽  
T Test ◽  
Comic Strips ◽  
Writing Ability ◽  
Paired Sample ◽  
And Control ◽  
Test Treatment

The purpose of this study was to investigate the significant effect of using comic strips strategy toward students’ reading comprehension and writing ability at MTsN 1 Pekanbaru. A Quasi-Experimental by Non-equivalent Pre-test and Post-test Group was applied as a designed for study. The sample was two classes (VIII 3 and VIII 4) consisting 20 students of treatment class, and 20 students of control class. The data were computed using SPPS 20.0 to analyze Independent sample t-test and Paired sample t-test. The finding of this study revealed that there was a significant effect on students’ reading comprehension by using comic strips strategy, it shown on paired sample t-test; treatment class was 77 and control class was 64.5, the hypothesis testing showed the result of post T-test -7.149, then score of sig.(2-tailed) is 0.000, if we act to null hypothesis (Ho) that is 0.05, it means that the score of sig.(2-tailed) was smaller than score of Ho. The data also revealed that there was a significant effect on students’ writing ability, it shown on paired sample t-test; treatment class was 79.6 and control class was 54.2, the hypothesis testing showed the result of post T-test -21.9, then score of sig.(2-tailed) is 0.000, if we act to null hypothesis (Ho) that is 0.05, it means that the score of sig.(2-tailed) was smaller than score of Ho. Therefore, the null hypothesis was rejected and the alternative hypothesis was accepted. From those data it can be summarized that there is a significant effect of using comic strips strategy on students’ reading comprehension and writing ability.

Hypothesis testing with error correction models

2021 ◽  
pp. 1-9
Author(s):  
Patrick W. Kraft ◽  
Ellen M. Key ◽  
Matthew J. Lebo
Keyword(s):  
Hypothesis Testing ◽  
Error Correction ◽  
Null Hypothesis ◽  
Alternative Hypothesis ◽  
Correction Coefficient ◽  
Correction Model ◽  
Long Run ◽  
Independent Variable ◽  
The Right ◽  
General Error

Abstract Grant and Lebo (2016) and Keele et al. (2016) clarify the conditions under which the popular general error correction model (GECM) can be used and interpreted easily: In a bivariate GECM the data must be integrated in order to rely on the error correction coefficient, $\alpha _1^\ast$ , to test cointegration and measure the rate of error correction between a single exogenous x and a dependent variable, y. Here we demonstrate that even if the data are all integrated, the test on $\alpha _1^\ast$ is misunderstood when there is more than a single independent variable. The null hypothesis is that there is no cointegration between y and any x but the correct alternative hypothesis is that y is cointegrated with at least one—but not necessarily more than one—of the x's. A significant $\alpha _1^\ast$ can occur when some I(1) regressors are not cointegrated and the equation is not balanced. Thus, the correct limiting distributions of the right-hand-side long-run coefficients may be unknown. We use simulations to demonstrate the problem and then discuss implications for applied examples.

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