Disclaimer

This blog in itself was a challenge. I wrote this message in a train while commuting to work. Nothing about it was planned. It was simply a response to an inner nudge to write this and push it out. And to avoid analysis paralysis, I didn’t do much editing on it, so please, pardon any error, just in case you see any.

Second disclaimer: the message from this blog is simple, but the examples and backing stories may appear research-dominant; please do not be taken aback by that, just read it and grab the message.

Thanks.

Embracing Challenges

This is 2026, and I was surprised by myself to realise that I’ve been dealing in the world of helping people with research needs for 12 years now.

I’d say that my first client was my mum, when I helped her to shorten her abstract from about 500 words to 250 word limit, without losing substance.

Since then, I’ve played a lot in the world of research.

Research designing, data collection, and data analysis.
Qualitative research and everything that comes with it.
Systematic reviews, meta-analyses, and meta-syntheses.
Controlled trials
Single cohort pre-post intervention studies
Manuscript reviewing and revising
Poster designing
Publication submission and logistics
Etc.
I’ve done them all.

Sometimes, I even wonder when exactly I formally learnt all of these, especially when I complete a task and look at the beauty I made.

But in reality, I learnt them by being in the middle of the challenge itself and having to fight my way out, of course, with the help of mentors.

The first time I did a meta-analysis, I was a complete novice and I did so much reading and reading before getting a hang of it.

Since then, I’ve done meta-analyses of both categorical and continuous outcomes. I’ve even meta-analysed single cohorts, and also handled non-dichotomous categorical outcomes. I’ve done meta-analyses where the right part of the forest plot favours the intervention and vice-versa. In fact, I’ve done over 30 meta-analyses for myself and my clients, and still counting.

Or is it thematic analyses. One day when I was consulting for a client and showing her how to code her data and eventually derive themes from them, she practically screamed and kept saying “thank you” over that call. She had failed her previous submission because she didn’t get her analysis and reporting right, despite having a wonderful robust data, which was well transcribed. Today, she’s now very proud of herself on how she has progressed and learnt thematic analysis better.

All of these, I learnt by taking the bull by the horn and facing it.

I’m a lover of knowledge, and even in the current world of AI dominance, I still task myself to learn things by first principle and not just consume the filtered and synthesised info that I get from the AI tools.

Truthfully, those AI tools have made things easier. Sometimes, my direction of learning starts with actually consuming their synthesised info, but at the end of the day, my true knowledge and expertise is gotten from deeper reading and researching, and eventual execution.

So, if you want to do or learn anything, sometimes, you don’t have to figure it all, just accept the challenge and dive into the water, and the universe will help your determination.

Trust me when I say the universe will help you. In my case, the universe helped me by giving me dripped challenges.

My first set of challenges were not meta-analyses or RCTs. It rather started with learning to read, understand, and interpret scientific articles (which was why I could handle my mum’s abstract), to now doing my undergraduate research project which was certainly a simple cross-sectional study that mostly required descriptive statistics.

Before I dabbled in the world of regression, I understood correlations first. T-tests, ANOVAs, Chi-square, Kaplan-Meier, etc, did not just appear magically.

The universe made sure my brain could handle them before it tossed the challenge to me.
So, yes, do your part, and leave the rest to the universe.

Good luck, and cheers,
STM

Final notes

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In the past, I’ve written about my thought process during a data visualisation task, you can read here. To view my latest research publication, you can see it here.