What is a major risk if data analysis methods are not specified in advance?

When data analysis methods are not specified in advance, a significant risk is the increased likelihood of biased results. This is primarily because decisions made during the analysis phase can be influenced by the data itself, leading researchers to unintentionally manipulate or select methods that support a preconceived hypothesis or desired outcome. Such biases can arise from the way in which data is interpreted or how certain variables are analyzed post hoc, rather than adhering to a predefined analysis plan.

For instance, without a specified analysis strategy, researchers might be swayed to exclude certain data points or choose specific statistical tests after noticing trends in the data. This can compromise the integrity of the research findings and undermine the objectivity that a pre-specified analysis method aims to uphold. In contrast, having a clear analysis plan enhances the transparency and reproducibility of research, reducing the risk of bias.

The other options, while relevant to research study design, do not capture the primary concern as directly as the likelihood of biased results does. Increased costs of data collection pertains more to logistical and financial aspects, while reduced clarity in research findings and limited research scope relate to outcome interpretation and completeness rather than methodological integrity. Thus, the focus on bias highlights a critical aspect of maintaining the rigor and credibility of research conclusions.