Biomedical data are heterogeneous in origin and impact. Data collection in biomedical science involves the investigator choosing between a series of dichotomous options. Experimental studies are important because they allow the effects of chosen parameters to be isolated and altered for assessment. However, experimentation involves significant investigator bias and is limited by morality especially for human studies. Observational investigations are important because they evaluate circumstances with minimal investigator involvement, but the sweep of the interpretations is consequently constrained. The most powerful analysis includes an integration of both experimental and observational data. Another important dichotomy in biomedical data involves subjective and objective data. Subjective data are inherently weak since they can be strongly affected by extraneous considerations. For instance, evaluation by questionnaire is not precise, but precision is not always needed. The danger of suicide can be assessed by subjective means with a questionnaire, and its deficiency in precision does not depreciate its value. There is no objective measure for suicidality for comparison. The distinction between the information gained by image-based analysis versus precise molecular assessment is another dichotomy of biomedical data with integration of both types providing the most powerful and valuable synthesis.