− | As mentioned earlier, it is important in any study not only that bias is minimised, but that the sample has sufficient [[Random variation#Confidence intervals and study precision|precision]] (in the case of descriptive studies) or [[Random variation#Hypothesis testing and study power|power]] (in the case of analytic studies). Both of these are closely related to the [[Random variation|random variability]] in any sample taken from a population. Although this can be reduced by increasing the sample size, a number of other considerations (usually logistical and economic considerations) will also be acting in order to reduce the number of samples which can realistically be taken. Statistical techniques are therefore available in order to calculate the required sample size. Counterintuitively, these require assumptions to be made regarding the final results of the study, as well as information regarding the required level of confidence, precision or power of the study. | + | As mentioned earlier, it is important in any study not only that bias is minimised, but that the sample has sufficient [[Random variation#Precision|precision]] and [[Random variation#Hypothesis testing and study power|power]] (in the case of analytic studies) to answer the question(s) for which the study is intended. Both of these are closely related to the [[Random variation|random variability]] in any sample taken from a population. Although this can be reduced by increasing the sample size, a number of other considerations (usually logistical and economic considerations) will also be acting in order to reduce the number of samples which can realistically be taken. Statistical techniques are therefore available in order to calculate the required sample size. Counterintuitively, these require assumptions to be made regarding the final results of the study, as well as information regarding the required [[Random variation#Confidence intervals|level of confidence]], precision or power of the study. |