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Deriving confidence in paleointensity estimates

Paterson, Greig A; Heslop, David; Muxworthy, Adrian R

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Determining the strength of the ancient geomagnetic field (paleointensity) can be time consuming and can result in high data rejection rates. The current paleointensity database is therefore dominated by studies that contain only a small number of paleomagnetic samples (n). It is desirable to estimate how many samples are required to obtain a reliable estimate of the true paleointensity and the uncertainty associated with that estimate. Assuming that real paleointensity data are normally...[Show more]

dc.contributor.authorPaterson, Greig A
dc.contributor.authorHeslop, David
dc.contributor.authorMuxworthy, Adrian R
dc.date.accessioned2015-12-07T22:52:52Z
dc.identifier.issn1525-2027
dc.identifier.urihttp://hdl.handle.net/1885/27612
dc.description.abstractDetermining the strength of the ancient geomagnetic field (paleointensity) can be time consuming and can result in high data rejection rates. The current paleointensity database is therefore dominated by studies that contain only a small number of paleomagnetic samples (n). It is desirable to estimate how many samples are required to obtain a reliable estimate of the true paleointensity and the uncertainty associated with that estimate. Assuming that real paleointensity data are normally distributed, an assumption adopted by most workers when they employ the arithmetic mean and standard deviation to characterize their data, we can use distribution theory to address this question. Our calculations indicate that if we wish to have 95% confidence that an estimated mean falls within a ± 10% interval about the true mean, as many as 24 paleomagnetic samples are required. This is an unfeasibly high number for typical paleointensity studies. Given that most paleointensity studies have small n, this requires that we have adequately defined confidence intervals around estimated means. We demonstrate that the estimated standard deviation is a poor method for defining confidence intervals for n < 7. Instead, the standard error should be used to provide a 95% confidence interval, thus facilitating consistent comparison between data sets of different sizes. The estimated standard deviation, however, should retain its role as a data selection criterion because it is a measure of the fidelity of a paleomagnetic recorder. However, to ensure consistent confidence levels, within-site consistency criteria must be depend on n. Defining such a criterion using the 95% confidence level results in the rejection of ∼56% of all currently available paleointensity data entries.
dc.publisherAmerican Geophysical Union
dc.sourceGeochemistry, Geophysics, Geosystems. G3
dc.subjectKeywords: Arithmetic mean; Confidence interval; Confidence levels; Consistency criteria; Data Selection; Data sets; Different sizes; Distribution theory; Geomagnetic fields; paleointensity; Paleointensity data; Rejection rates; Reliable estimates; Standard deviatio error analysis; paleointensity
dc.titleDeriving confidence in paleointensity estimates
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume11
dc.date.issued2010
local.identifier.absfor040406 - Magnetism and Palaeomagnetism
local.identifier.ariespublicationu4598381xPUB52
local.type.statusPublished Version
local.contributor.affiliationPaterson, Greig A, University of Southampton
local.contributor.affiliationHeslop, David, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationMuxworthy, Adrian R, Imperial College London
local.description.embargo2037-12-31
local.bibliographicCitation.issue7
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage15
local.identifier.doi10.1029/2010GC003071
local.identifier.absseo970104 - Expanding Knowledge in the Earth Sciences
dc.date.updated2016-02-24T11:12:06Z
local.identifier.scopusID2-s2.0-77955194031
local.identifier.thomsonID000280324500003
CollectionsANU Research Publications

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