Correction: Approximate Interval Estimation of the Difference in Binomial Parameters: Correction for Skewness and Extension to Multiple Tables

McGraw and Wong (1992) described an appealing index of effect size, called CL, which measures the difference between two populations in terms of the probability that a score sampled at random from the first population will be greater than a score sampled at random from the second. McGraw and Wong introduced this "common language effect size statistic" for normal distributions and then proposed an approximate estimation for any continuous distribution. In addition, they generalized CL to the n-group case, the correlated samples case, and the discrete values case. In the current paper a different generalization of CL, called the A measure of stochastic superiority, is proposed, which may be directly applied for any discrete or continuous variable that is at least ordinally scaled. Exact methods for point and interval estimation as well as the significance tests of the A = .5 hypothesis are provided. New generalizations ofCL are provided for the multi-group and correlated samples cases.

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Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample

Stats ◽

10.3390/stats2010009 ◽

2019 ◽

Vol 2 (1) ◽

pp. 111-120 ◽

Cited By ~ 1

Author(s):

Dewi Rahardja

Keyword(s):

Bayesian Inference ◽

Closed Form ◽

Bayesian Approach ◽

Interval Estimation ◽

Real Data ◽

Simulation Studies ◽

Posterior Distributions ◽

Binomial Data ◽

The Difference ◽

Data Subject

We construct a point and interval estimation using a Bayesian approach for the difference of two population proportion parameters based on two independent samples of binomial data subject to one type of misclassification. Specifically, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. For illustration, we applied our algorithm to a real data example. Finally, we conduct simulation studies to demonstrate the efficiency of our algorithm for Bayesian inference.

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Interval Estimation and Hypothesis Testing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.1717 ◽

2014 ◽

Vol 543-547 ◽

pp. 1717-1720

Author(s):

Da Yang

Keyword(s):

Hypothesis Testing ◽

Statistical Inference ◽

Mutual Influence ◽

Hypothesis Test ◽

Interval Estimation ◽

Point Of View ◽

Economic Management ◽

Engineering Technology ◽

Internal Relations ◽

The Difference

Mathematical statistics is a branch of mathematics has extensive application of interval estimation and hypothesis testing, which are two important problems of statistical inference. As two important statistical inference methods, interval estimation and hypothesis testing problem is more and more widely used in the field of economic management, finance and insurance, scientific research, engineering technology, the science of decision functions are recognized by more and more people. Can go further to establish mutual influence and communication between the interval estimation and hypothesis testing, can use the theory to explain the problem of interval estimation of parameter hypothesis test, this is an important problem to improve the statistical inference theory. Therefore, the basis on the internal relations between the interval estimation and hypothesis test for deep research, explain the problem of hypothesis testing and interval estimation from the point of view, discusses the difference and connection between the two.

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Comparative trials on hybrid walking systems for people with paraplegia: An analysis of study methodology

Prosthetics and Orthotics International ◽

10.3109/03093649909071645 ◽

1999 ◽

Vol 23 (3) ◽

pp. 260-273 ◽

Cited By ~ 5

Author(s):

M. J. Ijzerman ◽

G. Baardman ◽

H. J. Hermens ◽

P. H. Veltink ◽

H. B. K. Boom ◽

...

Keyword(s):

Study Design ◽

Statistical Power ◽

Outcome Measurement ◽

Interval Estimation ◽

Interrupted Time Series ◽

Foot Orthoses ◽

User Friendliness ◽

Ankle Foot Orthoses ◽

The Difference ◽

Comparative Trials

A new orthosis (SEPRIX) which combines user friendliness with low energy cost of walking has been developed and will be subject to a clinical comparison with conventional hip-knee-ankle-foot orthoses. In designing such comparative trials it was considered it may be worthwhile to use previous clinical studies as practical examples. A literature search was conducted in order to select all comparative trials which have studied two walking systems (hip-knee-ankle-foot orthoses) for patients with a complete thoracic lesion. Study population, intervention, study design, outcome measurement and statistical analyses were examined. Statistical power was calculated where possible. Of 12 selected studies, 7 were simple A-B comparisons, 2 A-B comparisons with a replication, 2 cross-over trials and 1 non-randomised parallel group design, the last of which was considered internally invalid due to severe confounding by indication. All A-B comparisons were considered internally invalid as well, since they have not taken into account that a comparison of two orthoses requires a control for aspecific effects (like test effects) which may cause a difference. Statistical power could only be examined in 4 studies and the highest statistical power achieved in one study was 47 %. It is concluded that statistical power was too low to be able to detect differences. Even analysis through interval estimation showed that the estimation of the difference was too imprecise to be useful. Since the majority of the surveyed papers have reported small studies (of only 4–6 patients), it is assumed that lack of statistical power is a more general problem. Three possibilities are discussed in order to enhance statistical power in comparative trials, i.e. multicentre studies, statistical pooling of results and improving the efficiency of study design by means of interrupted time series designs.

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