Applying the exact randomization test in experimental economics

Moir, Robert

doi:10.1002/(sici)1099-095x(199801/02)9:1<93::aid-env289>3.0.co;2-k

“…Note that non statistical inferences are very common in science when the results obtained from the studied subjects are generalized to all subjects that are similar as far as the important characteristics for designing the experiment are concerned. The population model is much more familiar than the randomization model, but it is the latter that makes sense in nearly all biomedical studies or in experimental economics . The permutation framework is justified because we may assume that under the null hypothesis of no difference due to grouping, the observed datum may be indifferently assigned to one of the groups (i.e.…”

Section: How To Make the Problem Of Unsound Inference Less Severementioning

confidence: 99%

“…The population model is much more familiar than the randomization model, but it is the latter that makes sense in nearly all biomedical studies 2 or in experimental economics. 36 The permutation framework is justified because we may assume that under the null hypothesis of no difference due to grouping, the observed datum may be indifferently assigned to one of the groups (i.e. the exchangeability assumption under the null hypothesis is met) and therefore conditional (on the observed data) inference can be drawn.…”

Section: How To Make the Problem Of Unsound Inference Less Severementioning

confidence: 99%

Does bad inference drive out good?

Marozzi

¹

2015

Clin Exp Pharma Physio

View full text Add to dashboard Cite

The (mis)use of statistics in practice is widely debated, and a field where the debate is particularly active is medicine. Many scholars emphasize that a large proportion of published medical research contains statistical errors. It has been noted that top class journals like Nature Medicine and The New England Journal of Medicine publish a considerable proportion of papers that contain statistical errors and poorly document the application of statistical methods. This paper joins the debate on the (mis)use of statistics in the medical literature. Even though the validation process of a statistical result may be quite elusive, a careful assessment of underlying assumptions is central in medicine as well as in other fields where a statistical method is applied. Unfortunately, a careful assessment of underlying assumptions is missing in many papers, including those published in top class journals. In this paper, we show that nonparametric methods are good alternatives to parametric methods when the assumptions for the latter ones are not satisfied. A key point to solve the problem of the misuse of statistics in the medical literature is that all journals have their own statisticians to review the statistical method/analysis section in each submitted paper

show abstract

Exact Randomization Technique

Moir

¹

2001

Encyclopedia of Environmetrics

0

View full text Add to dashboard Cite

The exact randomization technique is a distribution‐free, computationally‐intensive statistical method of dealing with small samples and uncertain distributions. The technique was first proposed by Fisher in 1935 as a method of dealing with small biological samples. Because of the computational demands, only the recent development of powerful desktop computers permits researchers to regularly use this technique. Suppose two sets of observations are recorded and labeled according to whether they come from a treatment or a control group. The concept of randomization is based on the observation that a conventional test statistic (e.g. a t statistic) can be interpreted as a typical draw from the distribution of test statistics that would be generated from randomly shuffling the treatment and control labels among the observed data. In fact, the exact randomization test is essentially a controlled example of bootstrapping.

show abstract

Exact Randomization Technique

Moir

¹

2012

Encyclopedia of Environmetrics

0

View full text Add to dashboard Cite

The exact randomization technique is a distribution‐free, computationally‐intensive statistical method of dealing with small samples and uncertain distributions. The technique was first proposed by Fisher in 1935 as a method of dealing with small biological samples. Because of the computational demands, only the recent development of powerful desktop computers permits researchers to regularly use this technique. Suppose two sets of observations are recorded and labeled according to whether they come from a treatment or a control group. The concept of randomization is based on the observation that a conventional test statistic (e.g. a t statistic) can be interpreted as a typical draw from the distribution of test statistics that would be generated from randomly shuffling the treatment and control labels among the observed data. In fact, the exact randomization test is essentially a controlled example of bootstrapping.

show abstract

Applying the exact randomization test in experimental economics

Cited by 4 publications

References 9 publications

Does bad inference drive out good?

Does bad inference drive out good?

Exact Randomization Technique

Exact Randomization Technique

Contact Info

Product

Resources

About