Confidence limits, error bars and method comparison in molecular modeling. Part 2: comparing methods

Abstract This chapter introduces plotting line graphs, bar charts, pie charts, box and whisker plots. It will troubleshoot the main areas where you are likely to encounter problems. It will show how to create log plots, add legends, error bars, notches and confidence limits, and introduce confidence limits and statistical testing. Examples are given, including bryophytes up a mountain; relationship between rural population size and the potential remaining intact forest; dietary differences between hornbill species (Buceros bicornis, Rhyticeros undulatus, Anthracoceros albirostris and Anorrhinus (Ptilolaemus) tickelli); and study of the level of trematode infection in various species of fish in Thailand.

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Plotting biological data in various ways.

Practical R for biologists: an introduction ◽

10.1079/9781789245349.0006 ◽

2021 ◽

pp. 37-78

Author(s):

Donald L. J. Quicke ◽

Buntika A. Butcher ◽

Rachel A. Kruft Welton

Keyword(s):

Population Size ◽

Rural Population ◽

Biological Data ◽

Statistical Testing ◽

Line Graphs ◽

Confidence Limits ◽

Trematode Infection ◽

Error Bars

Abstract This chapter introduces plotting line graphs, bar charts, pie charts, box and whisker plots. It will troubleshoot the main areas where you are likely to encounter problems. It will show how to create log plots, add legends, error bars, notches and confidence limits, and introduce confidence limits and statistical testing. Examples are given, including bryophytes up a mountain; relationship between rural population size and the potential remaining intact forest; dietary differences between hornbill species (Buceros bicornis, Rhyticeros undulatus, Anthracoceros albirostris and Anorrhinus (Ptilolaemus) tickelli); and study of the level of trematode infection in various species of fish in Thailand.

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Non-Gaussian error distribution of 7Li abundance measurements

Modern Physics Letters A ◽

10.1142/s0217732315501230 ◽

2015 ◽

Vol 30 (25) ◽

pp. 1550123 ◽

Cited By ~ 13

Author(s):

Sara Crandall ◽

Stephen Houston ◽

Bharat Ratra

Keyword(s):

Big Bang ◽

Error Distribution ◽

Confidence Limits ◽

Weighted Mean ◽

Big Bang Nucleosynthesis ◽

Double Exponential ◽

Error Bars ◽

Student’S T ◽

Non Gaussian ◽

T Distribution

We construct the error distribution of [Formula: see text] abundance measurements for 66 observations (with error bars) used by Spite et al. (2012) that give [Formula: see text] (median and [Formula: see text] symmetrized error). This error distribution is somewhat non-Gaussian, with larger probability in the tails than is predicted by a Gaussian distribution. The 95.4% confidence limits are [Formula: see text] in terms of the quoted errors. We fit the data to four commonly used distributions: Gaussian, Cauchy, Student’s t and double exponential with the center of the distribution found with both weighted mean and median statistics. It is reasonably well described by a widened [Formula: see text] Student’s t distribution. Assuming Gaussianity, the observed A(Li) is [Formula: see text] away from that expected from standard Big Bang Nucleosynthesis (BBN) given the Planck observations. Accounting for the non-Gaussianity of the observed A(Li) error distribution reduces the discrepancy to [Formula: see text], which is still significant.

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