I just found out this morning that my four weeks of work may have gone to waste.
Because the person who needs these experiments done was gone for a month (during which I started these experiments) (to Bangladesh, a country with pretty bad WiFi), he couldn’t tell me exactly how he wanted them done. He just assumed I knew exactly what training protocols to use for each treatment, and I did what I thought I was supposed to.
But don’t worry! All hope is not lost. This is just how I deliver bad news.
The faulty protocol may have diluted the effect we are looking for, but it also may not have. So I’ll just need to look at the data and see. Furthermore, even if we can’t see what we are looking for (which is the worst case scenario), it will take me just 2-3 weeks to redo the experiments properly (yes, one major error was that the experiments I was running were longer than they were supposed to be, so rerunning them will take about half the time it took initially).
I’ll just have my data in three weeks instead of in one.
I’ve learned two things from this. First, it’s important to check and double check and triple check the procedure before it’s done; it helps prevent these types of errors. Second, it’s best to do data analysis as the data comes in, and keep adding to it, to make sure that the data is isolating the effects of the variables you want it to. That way, if it isn’t, collection can be stopped midway and the procedure can be troubleshot a bit until it eliminates confounding variables.
On a completely different note, today is Lab Night for the Raymond Lab! Please come, it’s from 4-7 PM in the Munzer Auditorium of the Beckman Center. Lab members will be giving a talk on the research they conduct.
Because Lab Night is today, members of the lab have been practicing their talks quite often in recent weeks. We’ve been giving them feedback on what they can improve during their talks. Here’s what I’ve learned:
- When giving a talk to a group of people who may not be familiar with the field, present only what’s necessary for them to understand your research. Don’t include technicalities and details that may confuse them. If they want to know about them, they’ll ask.
- Translate scientific terminology into simpler terms without compromising accuracy. You can state the scientific conclusion and then say “in other words…” or “in simpler terms…”
- Clearly state what previous research states (and attribute it to the right people), and make it clear how you build off of it and what you did differently. What should people give you credit for?
- Don’t include a single graph or equation (or anything, really) that you’re not going to explain completely. It leaves the audience curious, unsatisfied, and with an itch in the back of their minds as to what it means.
- If you’re referencing a concept throughout the presentation (for example, a certain type of cell), use the same fonts/images every time when talking about it. It contributes to a sense of continuity in your presentation and helps the audience make the link to your previous explanations about it.