Department of Earth Sciences, Uppsala University, Uppsala, Sweden.
PLoS One. 2011 Apr 28;6(4):e19241. doi: 10.1371/journal.pone.0019241.
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.
线性趋势分析是许多科学学科的标准程序。如果数据量很大，即使数据远离趋势线分散，趋势也可能在统计上显著。本研究介绍并测试了一种称为统计意义的时间趋势质量标准，这是比高统计显著性更严格的趋势质量标准。将时间序列分为区间，并计算区间平均值。然后，从关于时间和区间平均值的回归中计算 r(2) 和 p 值。如果在任何这些回归中 r(2)≥0.65 且 p≤0.05，则认为趋势具有统计学意义。在所研究的来自不同科学学科的十个时间序列中，有五个显示出具有统计学意义的趋势。开发了一个 Microsoft Excel 应用程序（加载项），可以执行统计意义测试，并且可以提高测试的操作性。对于具有基本统计学技能的研究人员来说，所提出的区分具有统计学意义的趋势的方法应该相当简单，因此对于确定哪些趋势值得进一步分析，例如对于因果因素，可能很有用。该方法还可用于确定时间趋势的哪些部分特别值得关注。
