I went into marketing from a biology background, and I felt the transition was very easy. Collecting data in biology, especially on an ecosystem scale, is extre

I went into marketing from a biology background, and I felt the transition was very easy. Collecting data in biology, especially on an ecosystem scale, is extremely difficult, and your data has a lot of noise in it. How the data is collected and processed is almost more important than how the data was analyzed. Collecting and processing data is going to be extremely time consuming and expensive regardless of what you do, but you can make it easier on yourself through smart experimental design. P-values are still widely used for analysis in biology, to the chagrin of most statisticians. I don't think it is as much of a problem as it comes across as though. Bayesian reasoning is inherently included in the scientific process as part of the experimental design stage. The reliance on p-values becomes a problem when journalists report on research findings in a single paper that is "significant" because of p-values. The professionals in the field are capable of balanced analysis of p-value based research, but we end up with issues like anti-vaxxers when research is presented outside the field of professionals. Of course there are still ways the process can allow incorrect research to present itself. For one thing, journals are not interested in publishing studies that do not have "significant" results, which means that bad research can stick around for far too long. I personally think that the solution to these problems is open data, since so much of the research depends on how the data was manipulated and 'cleaned' prior to analysis.