Limits...
Elucidating the foundations of statistical inference with 2 x 2 tables.

Choi L, Blume JD, Dupont WD - PLoS ONE (2015)

Bottom Line: To many, the foundations of statistical inference are cryptic and irrelevant to routine statistical practice.The problem, not widely acknowledged, is that several different p-values can be associated with a single table, making scientific inference inconsistent.Accordingly, methods that are less sensitive to this discreteness - likelihood ratios, posterior probabilities and mid-p-values - lead to more consistent inferences.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.

ABSTRACT
To many, the foundations of statistical inference are cryptic and irrelevant to routine statistical practice. The analysis of 2 x 2 contingency tables, omnipresent in the scientific literature, is a case in point. Fisher's exact test is routinely used even though it has been fraught with controversy for over 70 years. The problem, not widely acknowledged, is that several different p-values can be associated with a single table, making scientific inference inconsistent. The root cause of this controversy lies in the table's origins and the manner in which nuisance parameters are eliminated. However, fundamental statistical principles (e.g., sufficiency, ancillarity, conditionality, and likelihood) can shed light on the controversy and guide our approach in using this test. In this paper, we use these fundamental principles to show how much information is lost when the tables origins are ignored and when various approaches are used to eliminate unknown nuisance parameters. We present novel likelihood contours to aid in the visualization of information loss and show that the information loss is often virtually non-existent. We find that problems arising from the discreteness of the sample space are exacerbated by p-value-based inference. Accordingly, methods that are less sensitive to this discreteness - likelihood ratios, posterior probabilities and mid-p-values - lead to more consistent inferences.

No MeSH data available.


See Fig. 1 for an explanation of these panels.In this example, the treatments have unequal sample sizes. For these tables, the marginal success total still tells us very little about ψ although it is slightly more informative than in balanced tables (see also Fig. 4).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4388855&req=5

pone.0121263.g003: See Fig. 1 for an explanation of these panels.In this example, the treatments have unequal sample sizes. For these tables, the marginal success total still tells us very little about ψ although it is slightly more informative than in balanced tables (see also Fig. 4).

Mentions: We examined the information about ψ contained in y+ under a wide variety of scenarios, including when the sample sizes are equal, small, large and extremely unbalanced with sparse cells. Fig. 1 and Fig. 2 show examples with equal smaller sample sizes, while Fig. 3 and Fig. 4 show examples with unequal sample sizes. In addition, in Fig. 1 and Fig. 3 the observed success rates are equal while in Fig. 2 and Fig. 4 they are not.


Elucidating the foundations of statistical inference with 2 x 2 tables.

Choi L, Blume JD, Dupont WD - PLoS ONE (2015)

See Fig. 1 for an explanation of these panels.In this example, the treatments have unequal sample sizes. For these tables, the marginal success total still tells us very little about ψ although it is slightly more informative than in balanced tables (see also Fig. 4).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4388855&req=5

pone.0121263.g003: See Fig. 1 for an explanation of these panels.In this example, the treatments have unequal sample sizes. For these tables, the marginal success total still tells us very little about ψ although it is slightly more informative than in balanced tables (see also Fig. 4).
Mentions: We examined the information about ψ contained in y+ under a wide variety of scenarios, including when the sample sizes are equal, small, large and extremely unbalanced with sparse cells. Fig. 1 and Fig. 2 show examples with equal smaller sample sizes, while Fig. 3 and Fig. 4 show examples with unequal sample sizes. In addition, in Fig. 1 and Fig. 3 the observed success rates are equal while in Fig. 2 and Fig. 4 they are not.

Bottom Line: To many, the foundations of statistical inference are cryptic and irrelevant to routine statistical practice.The problem, not widely acknowledged, is that several different p-values can be associated with a single table, making scientific inference inconsistent.Accordingly, methods that are less sensitive to this discreteness - likelihood ratios, posterior probabilities and mid-p-values - lead to more consistent inferences.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.

ABSTRACT
To many, the foundations of statistical inference are cryptic and irrelevant to routine statistical practice. The analysis of 2 x 2 contingency tables, omnipresent in the scientific literature, is a case in point. Fisher's exact test is routinely used even though it has been fraught with controversy for over 70 years. The problem, not widely acknowledged, is that several different p-values can be associated with a single table, making scientific inference inconsistent. The root cause of this controversy lies in the table's origins and the manner in which nuisance parameters are eliminated. However, fundamental statistical principles (e.g., sufficiency, ancillarity, conditionality, and likelihood) can shed light on the controversy and guide our approach in using this test. In this paper, we use these fundamental principles to show how much information is lost when the tables origins are ignored and when various approaches are used to eliminate unknown nuisance parameters. We present novel likelihood contours to aid in the visualization of information loss and show that the information loss is often virtually non-existent. We find that problems arising from the discreteness of the sample space are exacerbated by p-value-based inference. Accordingly, methods that are less sensitive to this discreteness - likelihood ratios, posterior probabilities and mid-p-values - lead to more consistent inferences.

No MeSH data available.