Difference between revisions of "Inferential statistics"
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| − | In many epidemiological studies, it is not possible to include every individual in a population. Rather, a [[Sampling strategies|sample]] of individuals is collected. This may be take the form of a [[Study design#Surveys|survey]], a [[Study design#Cross sectional studies|cross-sectional study]], a [[Study design#Experimental studies|randomised controlled trial]], and so on. The important issue is that '''not every individual in the [[Sampling strategies#Source population|source population]] is included''', which means that [[Random error| random, or sampling, error]] and [[Bias#Selection bias|biases]] may be introduced. These affect our ability to extrapolate our results (whether [[Study design#Descriptive studies|descriptive]] or [[Study design#Analytic studies|analytic]] in nature) to the source population. However, the aim of most studies is to draw some conclusion about the source population, using the results obtained from the sample. This | + | In many epidemiological studies, it is not possible to include every individual in a population. Rather, a [[Sampling strategies|sample]] of individuals is collected. This may be take the form of a [[Study design#Surveys|survey]], a [[Study design#Cross sectional studies|cross-sectional study]], a [[Study design#Experimental studies|randomised controlled trial]], and so on. The important issue is that '''not every individual in the [[Sampling strategies#Source population|source population]] is included''', which means that [[Random error| random, or sampling, error]] and [[Bias#Selection bias|biases]] may be introduced. These affect our ability to extrapolate our results (whether [[Study design#Descriptive studies|descriptive]] or [[Study design#Analytic studies|analytic]] in nature) to the source population. However, the aim of most studies is to draw some conclusion about the source population, using the results obtained from the sample. This requires the use of statistical methodology in a process known as '''inferential statistical analysis''', and is commonly used in epidemiological investigations.<br> |
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| + | Inferential statistical methods cannot be used to correct for the presence of '''selection biases''' introduced during sample collection. These should either be minimised during data collection, or if they cannot be avoided, they should be discussed in the analysis report. However, statistical methods are available in order to account for '''random error''' in a sample. Due to their random nature, these errors can be quantified if the underlying data is known. Of course, as this is never the case when sampling from populations, sample estimates are used to approximate the underlying population parameters. The most common application of inferential statistics is in the calculation of '''[[Random error#confidence intervals|confidence intervals]]'''.<br> | ||
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[[Category:Veterinary Epidemiology - Statistical Methods|D]] | [[Category:Veterinary Epidemiology - Statistical Methods|D]] | ||
Revision as of 14:11, 9 May 2011
In many epidemiological studies, it is not possible to include every individual in a population. Rather, a sample of individuals is collected. This may be take the form of a survey, a cross-sectional study, a randomised controlled trial, and so on. The important issue is that not every individual in the source population is included, which means that random, or sampling, error and biases may be introduced. These affect our ability to extrapolate our results (whether descriptive or analytic in nature) to the source population. However, the aim of most studies is to draw some conclusion about the source population, using the results obtained from the sample. This requires the use of statistical methodology in a process known as inferential statistical analysis, and is commonly used in epidemiological investigations.
Inferential statistical methods cannot be used to correct for the presence of selection biases introduced during sample collection. These should either be minimised during data collection, or if they cannot be avoided, they should be discussed in the analysis report. However, statistical methods are available in order to account for random error in a sample. Due to their random nature, these errors can be quantified if the underlying data is known. Of course, as this is never the case when sampling from populations, sample estimates are used to approximate the underlying population parameters. The most common application of inferential statistics is in the calculation of confidence intervals.