One of the challenges that could occur when performing data science research is the potential for sample bias. Smith and Noble (2014) argue that sample bias occurs when the sample does not represent the overall population being studied. Sampling bias can be caused by several things, including using non-random sampling techniques or the impact of confounding variables that were not considered during sample selection. The researcher's conclusions might not apply or generalizable to a larger population when the sampling procedure is biased, leading to false or misleading findings. One way to address sample bias is to design sampling methods that ensure that individuals or data points are randomly chosen from the population. The population can be segmented into subgroups based on particular traits using stratified sampling, and samples can then be randomly chosen from within each subgroup to accurately represent the overall population.

Another challenge that could occur when performing data science research is having an insufficient sample size. The statistical power of the analysis might not be strong enough to identify significant patterns or connections if the sample size is too small. As a result, the study might not offer any important insights or conclusions, which could result in inconclusive or incorrect findings. To solve this, a power analysis can be carried out beforehand to help data scientists prepare for the study. The power analysis, which considers several factors, including the effect size, the targeted significance level, and statistical power, establishes the necessary sample size. Researchers can ensure that their study is appropriately powered to yield relevant results by determining the minimal sample size necessary to detect significant effects. As a result, they can choose the ideal range for the sample size.