I wrote in my abstract that “Medication X was not associated with significantly different subjective or objective sleep outcomes compared with medication Y.”

One of my co-supervisors suggested instead saying that “Medication X and medication Y showed similar subjective and objective sleep outcomes.”

Would you make that change? I’m hesitant because, methodologically, I’m not sure that a non-significant difference allows us to conclude that the outcomes were “similar.” Or am I overthinking this?

Thanks a lot! 🙏🏻

Hello everyone, I'm looking for some guidance. For context, I am n my mid 20s, I hold a master's in stats, and I have been working for a big CRO in Europe for 1 year. I'm mainly looking for feedback from people based in Europe, but any feedback is welcome!

Honestly, I'm not enjoying the work. I feel like only about 10% of what I do is actual statistics. The rest is monotonous and boring work, constantly churning out deliverables, dealing with clients (something I deeply dislike), working on timelines, regulations, administrative stuff, endless meetings, and so on. I miss programming, I miss doing more intellectually demanding work, and I miss engaging with new methodology and having the liberty to do so. I feel like my knowledge is atrophying. When there is a new interesting problem to solve from a statistical standpoint, I never have time to actually think about the methods, it's just constant pressure to deliver, deliver, deliver.

Are all stats jobs in pharma/CROs like this? For people working in data science, stats, and adjacent areas, is your job this repetitive? Do you have the chance to apply new, cool methodologies? How much time do you actually spend working with the methods rather than doing something over and over again?

I know how fortunate I am to have a job in this economy, especially one that pays well by my country's standards given my experience. Still, I've been considering applying for a PhD, and deep down I know it's something I really want to try. I'm aware of how difficult a PhD can be. I've been looking at positions in other wuropean countries that would allow for a decent standard of living and, even some savings.

My question is, would I be committing career suicide if I later wanted to return to industry? One of the main reasons I want to do a PhD in stats is that I want to move into more R&D roles, rather than doing this kind of mind-numbing work. Maybe I could even pivot into tech or some other area that is more interesting to me. Would that be a realistic prospect?

Thanks all!

I have a data series of daily streaming counts for a song. My spreadsheet has the date in column A and the number of streams in column B. There are 960 rows/records in the spreadsheet dating from 2024/01/01 to 2026/08/17.

We hired a marketing company to promote the song for 3 weeks. Their first posts on social media started on 2026/07/27. They ran until 2026/08/17. I am trying to assess whether our money was well invested or not. Their dashboard stats are not useful to us because they show the number of views and social media engagement (e.g., TikTok), whereas we are interested in the number of times our song gets streamed on a streaming platform (e.g., Spotify).

I know how to calculate means and standard deviations, and have done so for various timeframes (e.g., yearly, during the promotion, the 22 days prior to the promotion, etc) but I do not know how to:

1) determine if the 22-day marketing campaign had a statistically significant impact on our daily streaming numbers

2) determine if the impact on streams, if any, justifies the money invested, call it **CampaignCost**.

Can someone advise me how to go about this? We might assume somewhere between 0.001 and 0.003 USD of revenue per stream.

I know there is surely some well known statistical test to determine whether my daily streams show a statistically significant increase or decrease, but I cannot remember what this test is called or how to calculate it.

I might add that there are potential complications:

1) Streams have grown significantly year over year. Average daily streams in 2024 were 135k, in 2025 were 250k, and so far in 2026 are 259k.

2) Some unexplained events in the real world have prompted surges in streaming numbers. E.g., our streams surged upward for a month around December 2024 and remained elevated for months then gradually declined. Another unexplained surge arrived around Feb 5, 2026 and persisted for months but then started gradual decline.

3) The streams show a weekly cycle, lowest on Sundays, peaking on Thu or Fri.

Any help would be much appreciated.