In psychology, statistics are studied by most, and disliked by most as well. Rarely will you hear "Awesome! I’ve got Advanced Multivariate Stats this semester!" uttered in graduate schools. The problem for many psychologists and psychology students is simply that they have entered the field to do clinical work, and statistics doesn’t seem all that relevant; it’s sort of like taking a play therapy class if you work in corrections. In some cases, people enter this field because of the interpersonal nature, and non-mathematical nature, of clinical work. For others, math was a negative experience when they were younger, and they have a negative view of it, even though they are now older, and the statistics can be learned in a much more applied than theoretical manner. That is, it’s much harder to grasp the relevance of trigonometry as a teen, than to learn about a particular statistical procedure with an accompanying journal article related to one’s area of interest.
In addition, the advent of statistical software and electronic data gathering in general have rendered statistics and research much more digestible than those dark, pre-computer days. I’m not old enough to remember research before any computers at all, but I do remember stories from grad school, when teachers used to discuss how they would need to create numerous "punch cards" with their data in order to complete calculations, and run the cards through what has been described as precursors to computers; I shudder at the thought, but back then, there was no alternative.
It is this technological advancement that actually makes statistics so much more interesting than before. It’s not only that "doing stats" is easier; as long as you know the right statistics to use, the computer does all the work; the more significant development is that much more complex questions can be asked and answered, due to the wider variety of statistical procedures now available.
Back in the good old days, it seems much multivariate statistical ideas, if they even existed, were theoretical. That’s because the math involved in calculating, by hand, a significant amount of data with these more complicated formulas would take a lifetime. T-tests and, if really bold, ANOVAs were the way to go, if you ever wanted to finish something. But those tests limit the range of ways to examine data. Nowadays, with the processing power available, virtually anyone with access to data can answer questions earlier scientists could have only dreamed about.
Confession: I’ve always been more of a digester of statistics and research, rather than an active researcher. I don’t pretend to even be an expert or specialist in statistics, just someone who finds research important to review, and has occasionally considered crunching my own data at some point (beyond the obligatory dissertation, which was completed long ago). I’m hoping I am at a point where actual data collection related to relevant questions I have is not only available, but possible within the purview of my job. However, until it’s done, there’s really no need to talk about what I "might" do; I just need to do it.
Anyway, for those of you considering the field of mental health, or are already in it in some capacity, there’s no need to shy away from statistics. Yes, sometimes the stuff gets complex, and goes over my head, but understanding the fundamentals can help with understanding research, which can, in turn, pique your interest in your particular clinical, developmental, etc. area. I know it works this way for me.