I Have a large set of data, but each value was estimated. I need to measure how accurate the estimations are without measuring every point. If I can somehow use statistical sampling or somthing and measure a small percentage of the points then somehow statisticly indicate how accurate the original data set is to determine if I need to measure more points to check it or leave it alone.
What is the scientific method for this?
Thank you much...
2006-12-08
16:52:19
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3 answers
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asked by
hmmm
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Science & Mathematics
➔ Mathematics
My data set is about n=50. They are length measuments in miles that were given to me to check. I'm not sure how they were originally gathered perhaps by roller or off a map. I have taken about 20 measurements by roller and compared to the existing and they seem fairly accurate. But I need a way to prove this statisticly to show why I selected to check that many and not half or more or less or all. Its very time consuming to check them all and this won't be the last time I'm asked to do this. Since the data is road lengths then there is not pattern or curve its just a list of roads and their lengths so the data is totally random as far as patterns. I just need to prove statisticly that its OK not to check all of them, why its OK, how I arrived at the number to actually measure, and if the corelation or whatever its called the accuracy is good enough over that sample to show that I don't have to check them all. Are there standard for this, like if the error is less that a certain amount
2006-12-08
17:47:26 ·
update #1