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What's more general than a whole population?

Alexander N - Emerg Themes Epidemiol (2015)

Bottom Line: Statistical inference is commonly said to be inapplicable to complete population studies, such as censuses, due to the absence of sampling variability.With reference to the social science literature, the current paper explores the circumstances under which statistical inference can be meaningful for such studies.It concludes that its use implicitly requires a target population which is wider than the whole population studied - for example future cases, or a supranational geographic region - and that the validity of such statistical analysis depends on the generalizability of the whole to the target population.

View Article: PubMed Central - PubMed

Affiliation: MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.

ABSTRACT
Statistical inference is commonly said to be inapplicable to complete population studies, such as censuses, due to the absence of sampling variability. Nevertheless, in recent years, studies of whole populations, e.g., all cases of a certain cancer in a given country, have become more common, and often report p values and confidence intervals regardless of such concerns. With reference to the social science literature, the current paper explores the circumstances under which statistical inference can be meaningful for such studies. It concludes that its use implicitly requires a target population which is wider than the whole population studied - for example future cases, or a supranational geographic region - and that the validity of such statistical analysis depends on the generalizability of the whole to the target population.

No MeSH data available.


Related in: MedlinePlus

In a whole population study, the sample (dashed circle) has become so large as to coincide with the population (solid circle). The use of confidence intervals, p values, or similar probability statements implies a claim to generalizability to some group beyond the study population: namely the target population, represented by the area outside the two circles. As in Fig. 1, presence or absence of a characteristic is indicated by darker or lighter shading. Authors of whole population studies often do not try to delimit their generalizability. In the figure, such indeterminacy is represented by the target population fading away from the original population: we may not know to what spatial or temporal range the findings may be applicable. Similarly, we may not be able to judge whether the prevalence of the characteristic remains similar in the target population, rather than increasing or decreasing (as it does to the bottom left and top right, respectively, of the figure)
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Fig2: In a whole population study, the sample (dashed circle) has become so large as to coincide with the population (solid circle). The use of confidence intervals, p values, or similar probability statements implies a claim to generalizability to some group beyond the study population: namely the target population, represented by the area outside the two circles. As in Fig. 1, presence or absence of a characteristic is indicated by darker or lighter shading. Authors of whole population studies often do not try to delimit their generalizability. In the figure, such indeterminacy is represented by the target population fading away from the original population: we may not know to what spatial or temporal range the findings may be applicable. Similarly, we may not be able to judge whether the prevalence of the characteristic remains similar in the target population, rather than increasing or decreasing (as it does to the bottom left and top right, respectively, of the figure)

Mentions: The current paper argues that the problem of inferential methods in whole populations is most usefully understood as one of generalizability (Fig. 2). Thinking in these terms helps show that inferential methods are meaningless for some situations while for others their use is at least arguable (see Table 1).


What's more general than a whole population?

Alexander N - Emerg Themes Epidemiol (2015)

In a whole population study, the sample (dashed circle) has become so large as to coincide with the population (solid circle). The use of confidence intervals, p values, or similar probability statements implies a claim to generalizability to some group beyond the study population: namely the target population, represented by the area outside the two circles. As in Fig. 1, presence or absence of a characteristic is indicated by darker or lighter shading. Authors of whole population studies often do not try to delimit their generalizability. In the figure, such indeterminacy is represented by the target population fading away from the original population: we may not know to what spatial or temporal range the findings may be applicable. Similarly, we may not be able to judge whether the prevalence of the characteristic remains similar in the target population, rather than increasing or decreasing (as it does to the bottom left and top right, respectively, of the figure)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4549103&req=5

Fig2: In a whole population study, the sample (dashed circle) has become so large as to coincide with the population (solid circle). The use of confidence intervals, p values, or similar probability statements implies a claim to generalizability to some group beyond the study population: namely the target population, represented by the area outside the two circles. As in Fig. 1, presence or absence of a characteristic is indicated by darker or lighter shading. Authors of whole population studies often do not try to delimit their generalizability. In the figure, such indeterminacy is represented by the target population fading away from the original population: we may not know to what spatial or temporal range the findings may be applicable. Similarly, we may not be able to judge whether the prevalence of the characteristic remains similar in the target population, rather than increasing or decreasing (as it does to the bottom left and top right, respectively, of the figure)
Mentions: The current paper argues that the problem of inferential methods in whole populations is most usefully understood as one of generalizability (Fig. 2). Thinking in these terms helps show that inferential methods are meaningless for some situations while for others their use is at least arguable (see Table 1).

Bottom Line: Statistical inference is commonly said to be inapplicable to complete population studies, such as censuses, due to the absence of sampling variability.With reference to the social science literature, the current paper explores the circumstances under which statistical inference can be meaningful for such studies.It concludes that its use implicitly requires a target population which is wider than the whole population studied - for example future cases, or a supranational geographic region - and that the validity of such statistical analysis depends on the generalizability of the whole to the target population.

View Article: PubMed Central - PubMed

Affiliation: MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.

ABSTRACT
Statistical inference is commonly said to be inapplicable to complete population studies, such as censuses, due to the absence of sampling variability. Nevertheless, in recent years, studies of whole populations, e.g., all cases of a certain cancer in a given country, have become more common, and often report p values and confidence intervals regardless of such concerns. With reference to the social science literature, the current paper explores the circumstances under which statistical inference can be meaningful for such studies. It concludes that its use implicitly requires a target population which is wider than the whole population studied - for example future cases, or a supranational geographic region - and that the validity of such statistical analysis depends on the generalizability of the whole to the target population.

No MeSH data available.


Related in: MedlinePlus