An operational definition of a statistically meaningful trend

Linear trend analysis of time series is standard procedure in many scientific disciplines. If the number of data is large, a trend may be statistically significant even if data are scattered far from the trend line. This study introduces and tests a quality criterion for time trends referred to as s...

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Bibliographic Details
Published in:PloS one 2011-04, Vol.6 (4), p.e19241
Main Authors: Bryhn, Andreas C, Dimberg, Peter H
Format: Article
Language:English
Subjects:
Alzheimer's disease
Alzheimers disease
Analysis
Chlorophyll
Climate Change - statistics & numerical data
Criteria
Data processing
Earth science
Earth sciences
Geovetenskap
Gross Domestic Product - trends
Hypotheses
Hypothesis testing
Mathematical analysis
Mathematics
Methods
Models, Statistical
NATURAL SCIENCES
NATURVETENSKAP
Nitrogen
Phosphorus
Probability
Regression analysis
Significance
Software
Spreadsheet software
Statistical analysis
Statistical significance
Statistics
Statistik
Time series
trend
Trend analysis
trendanalys
Trends
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Summary:Linear trend analysis of time series is standard procedure in many scientific disciplines. If the number of data is large, a trend may be statistically significant even if data are scattered far from the trend line. This study introduces and tests a quality criterion for time trends referred to as statistical meaningfulness, which is a stricter quality criterion for trends than high statistical significance. The time series is divided into intervals and interval mean values are calculated. Thereafter, r(2) and p values are calculated from regressions concerning time and interval mean values. If r(2) ≥ 0.65 at p ≤ 0.05 in any of these regressions, then the trend is regarded as statistically meaningful. Out of ten investigated time series from different scientific disciplines, five displayed statistically meaningful trends. A Microsoft Excel application (add-in) was developed which can perform statistical meaningfulness tests and which may increase the operationality of the test. The presented method for distinguishing statistically meaningful trends should be reasonably uncomplicated for researchers with basic statistics skills and may thus be useful for determining which trends are worth analysing further, for instance with respect to causal factors. The method can also be used for determining which segments of a time trend may be particularly worthwhile to focus on.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0019241