样品不需要随机是有效的,它可以帮助,但这并不是最重要的尤其是详尽的,一致的,并且有一个大样本的大小,尤其是在处理观察性研究。比随机样本代表性是不同的。记住我们不是要达到全球气温最高的精确度在一个瞬间,我们正试图测量温度的变化在一个大跨度的时间。因此位置比随机化更重要位置的一致性。的采样点不要移动是至关重要的,我们知道温度是影响区域条件如果样本re-randomized(移动),每个测量将使它更加准确而不是相反。还记得被测量,* *改变,因为采样点没有移动变化将是准确的,因为它本质上是分层抽样。如果我测量发动机温度的变化例如我并不想测量每次在一个不同的点,只要点(位置)是一致的样本将保持高精度。随机抽样是不准确的,因为它会邀请混淆,因为我们知道温度的分布在整个引擎(或世界)并不是随机的。测量之间的任何位置的改变将邀请混淆数据。几乎没有科学使用一个真正的随机样本,它是不可能的。 Consider an example , say you are trying to measure the temprature change in an engine over time. Where on the engine I attach my sensors does not matter as long as I do not move them, especially if I put many sensors on it. I could put thirty sensors all on the left side engine, and it would measure the change in temperature very accurately, compared to moving the sensors between every measurement. Don't fall for the perfect solution fallacy. Also remember this is an observational/descriptive study by its very nature. Each point on the map is more like a repetition, the real independent is the time at which they are sampled, which is either stratified or clustered depending on which study you refer too. Note that multiple sets of data points are also [compared][1], NOAA, BEST, etc. are each independent data sets that can be compared, and show the same [pattern.][2] high and low are used for measurements because that is all that was recorded in the oldest measurements, so changing the format would require throwing out all that data, drastically shortening the sample size (loosing more than half the time span). In this case the accuracy gained by the much larger number of samples is more than would be gained by a random or grid location. Random is rarely possible with historic data which is why the size and consistency of the data set is so important. The nice thing is these are also [compared][3] to other sampling methods on other time scales to test to see if they are show the same [pattern][4]. Historic scientists are aware of the limitations of their data which is why independent verification is so important. Now consider [ice core data][5], I was surprised when you said surface temperature was the most used, I see ice core data far more often, because it records a much longer span of time, and records other things (like CO2 cont) as well. Again each core is a repetition and the core can be sampled in a random or stratified way, stratified is the most common because it is more exhaustive in a core sample. Ice cores are also compared to ice cores for m other locations. Another consideration is cross-comparison, that is the use of multiple independent forms of measure, ice core compared to satellite, compared to surface, etc. Dozens of different forms of measurements/experiments are compared and show the same pattern. [This][6] is probably one of the best overviews of the science I have seen. It is a little old (2013) so if anyone has seen a more recent version I would love to use it instead. [1]: http://www.pnas.org/content/105/36/13252.abstract [2]: http://journals.ametsoc.org/doi/abs/10.1175/BAMS-88-9-1383 [3]: https://www.ncbi.nlm.nih.gov/pubmed/23471405 [4]: http://www.nature.com/nature/journal/v433/n7026/full/nature03265.html [5]: http://www.nature.com/nature/journal/v399/n6735/full/399429a0.html [6]: http://www.climatechange2013.org/images/report/WG1AR5_SPM_FINAL.pdf