In 2012 I was working in an independent medical research institute. Senior colleagues thought I was capable of a PhD. That level of academic achievement was somewhat mythical to me (my father never finished primary school), but it was an irresistible idea. I could spend hours with data and be called Dr. at the end –  sign me up!

As it turned out, I could do it. I submitted a PhD by publication in the final university business hours of 2015.

Twelve months after submission, a somewhat messy year and protracted examination, my PhD was conferred and I had accepted a great job opportunity. I was delighted to graduate and felt, as many do, enormous relief and pride. A PhD is a pinnacle, the highest degree. I was formally and rigorously educated, exhausted, and poor. On graduation day (a hairdryer hot Melbourne December day) I celebrated with my considerably neglected partner. We had marvellous pizza and a cold drink and I thought of the immortal line from Shrek: “that’ll do donkey, that’ll do”.

My postdoctoral job is entirely focussed on data analysis and although I am many things, nurse, a doctor, probably an epidemiologist, and now a Research Fellow, I am not a statistician. The gaps in my knowledge started to come into focus and I found myself thinking about career doors that were not open to me. In the six months following my graduation I realised that my PhD did not deliver something I needed because I never asked it to.

Many people are lifelong learners and career shapeshifters. They trot down the side of the PhD mountain, shake it off, and head on up the path in front of them with ease, even if the path is tangential. But I found it upsetting to think that I was dissatisfied with my qualifications when the opportunity cost – and actual cost – of my PhD seemed very high.  I chastised myself for not thinking through my PhD better, being wasteful, indulgent and even cowardly. I burnt a lot of energy for the first half of 2017: kicking myself, cursing myself, talking myself in and out of things.

Facing up to the fact that I needed (probably more like wanted) to do more education as opposed to consolidating a postdoc career, felt like failure. I fell into some kind of a postdoctoral hole.

A friend recently blogged about parsing thoughts with the first screening question being, “Is it true?”. There two more but one is enough self-reflection for me. None of my thoughts about my education and career to date were true. Pretzeling myself into someone I did not want to be, to save face, was tiring.  A colleague, friend and straight up nice human once told me, a PhD never gets taken away from you. I needed to stop trying to take my PhD away from myself. It was marvellous achievement and wanting to reactivate my student number did not diminish its value.

When I floated the idea of doing a Masters to gain formal training in statistics with a select few of my friends and family I got a pause, followed by, “what?”.  I quietly began browsing university handbooks ‘just to see’ what was on offer. Perusing through courses, checking entry requirements and sizing up prerequisites was confronting. The first subject of a Masters in Biostatistics is Maths Background for Biostatistics. That sounds bad, especially for someone who did not complete high school maths. Compounding the terror, I had tried to do this subject during my PhD and did not get past week two. The voices of doubt were very loud at this point, a choir really, a gospel choir.

I have started my fourth degree, a Masters in Biostatistics, because I wanted to: 231 days after receiving my PhD and after approximately seven months of internal warfare and nausea. Pulling up out of the emotional hole and facing a fear of maths aside, it was a difficult choice. I work full time work and was facing a part-time load and full fees. Financial and personal resources have a higher value now, I guess an age effect. But I love statistics and I don’t want to boxed into one field nor be standing on the tectonic plates of grant funding.

I want to do much better analysis and work in collaborative teams on hard problems.  Finally, there is ongoing furore about the reproducibility of research, fraudulent research and the use p-values. One commentator offered an argument I do agree with. Part of the solution is training researchers in statistics and data analysis, and I would like to be part of that solution (an argument for more focus on coursework and training in statistics in Australian PhDs is for another time).

Author Bio:  Anna Wilkinson is a Research Fellow at Cancer Council Victoria, Melbourne, Australia.