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The greater the sampling error the greater the uncertainty about the results and therefore the more careful you need to be in the interpretation.

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Q: How does a sampling error affect the interpretation of your data?
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Continue Learning about Other Math

What is statistical data?

Statistical data are numbers that are based on a sampling of a population to predict an outcome. The accuracy depends on the sample number and error and confidence and other analysis.


What is the importance of combining different data collection techniques?

The importance of combining different data collection techniques balances the strengths and weaknesses of each other. It helps reduce non-sampling error and ensures improvement in data evaluation.


Why is it important to have two sets of temperature data?

If both sets are in agreement, it is a good indication each is accurate. If, on the other hand, there is great disparity between the two sets, we may conclude there is some significant error in our data gathering or sampling technique. The NASA GISS data (see link) include both a land/sea temperature index and temperature measurements from meteorological stations.


What is the branch of math concerned with collection interpretation of data?

STATISTICS


Why does sampling error occur?

The list is very long! In sampling error, I include all aspects of data collection. Samples (and not the full population) are taken in observational and experimental studies. The sample size may be a problem. In some cases, it may impossible to correct. If I am studying some rare occurrence, say hurricanes with winds over 280 mph or incidences of mad cow disease, the number of observations is fixed. Other times, there is inadequate time or budget to sample adequately. Sampling error can occur because of the way a sample is taken. This is very true of marketing surveys, which may be taken at time when they are more likely to survey one segment of the population. Or they are taken in one location that is not representative of the general population. A voluntary survey, or convenience survey may also be biased. The manner in which questions are posed, can introduce bias. Inadequate quality checking also contributes to sampling error. This is true whether the data collection is done by humans, or instruments, such as a testing laboratory. If a particular instrument is improperly calibrated, all measurements can be questionable. Finally, there are many means of purposely introducing bias into collected data in order to show "factual evidence" of preconceived ideas. The time frame or location where data is to be collected may be done to build in a particular bias. You will probably find more examples by searching the internet.