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,和有一个大的样本容量。也不认为这是一个观察性研究实验。记住我们不是要达到全球温度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。Thsu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将邀请混杂因为我们知道温度的分布在引擎(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。同样在发动机我附加传感器并不重要,只要我不要移动他们,特别是如果我把许多传感器。 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. high and low are used for measurements becasue 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 to other sampling methods on other time scales to test to see if they are show the same pattern. Historic scientists are aware of the limitations of their data which is why independent verification is so important. Now consider [ice core data][1], I was surprised when you said surface temprature was the most used, I see ice core data far more often, becasue 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 becasue it is more exhaustive in a core sample. Another consideration is cross-comparison, that is the use of multiple independent forms of measure, dozens of different forms of measurements/experiments are compared and show the same pattern. [1]: http://www.nature.com/nature/journal/v399/n6735/full/399429a0.html