Video: Tips to avoid research errors at the design stage

Start by addressing this challenge right from the design stage of the experiment. Use randomized data or select randomized samples. This helps minimize data bias, giving you a more balanced and representative dataset. 

Another method is to define the exclusion criteria before the initiation of study. Excluding certain subjects, especially from medical or social science experiments, can ensure that the focus remains on a relatively homogenous group of participants. 

Stratification of samples is another technique, wherein data are collected by dividing the population into subgroups. Stratified sampling is especially suited for large populations where the data needs to be represented across the entire spectrum. When each subgroup is sufficiently represented, the measurement accuracy is improved.