The mechanic analogy is one of my favourites. In terms of the difference between clinicians and scientists, scientists are the car designers - the experts in each part of the the car, what it does, how it works, why the design is better than alternatives, and so on.

The clinician in the mechanic who changes the oil and occasionally fixes the tracking...

The mechanic analogy is one of my favourites. In terms of the difference between clinicians and scientists, scientists are the car designers - the experts in each part of the the car, what it does, how it works, why the design is better than alternatives, and so on.

The clinician in the mechanic who changes the oil and occasionally fixes the tracking...

The bit about "proper doctor"/medical doctor terminology hit a raw nerve with me - I already think that basic/lab-based scientists get a pretty raw deal, and it doesn't help when I speak to friends who are medics, and think that you can do a PhD in three years by getting in to work at 11 and leaving at 3. I suppose the public perception is that clinicians do everything, and scientists are just weirdos who spend their lives locked away in a lab. But yes, the term "doctor" is purely honorary for medics, unless they also have PhDs, and they should be reminded of this!

I love EndNote. Quite simply, it's saved me a huge amount of time, and helps me keep all my reading in order. Some people use it for finding papers to read too - I don't, as I prefer to use Medline or Web of Science. The only other bibliography software I have experience of is RefManager. This is made by the same people as EndNote, and I reckon it'll be phased out over the coming years, as everyone I know seems to use EndNote.

Maybe I should add that my field is biomedical, so the vast majority of my statistics are relating to things in dishes, and I think it's unlikely that I'd habve to go much beyond t-test, ANOVA, and their non-normally distributed equivalents. That said, epidemiology is something I could move into in the future, so anything which gives me extra knowledge now would be useful.

Well, having looked at the Field and Pallant books, I definitely prefer the former - the Pallant book looks pretty hard to read. I'm still interested in the Dytham book too though...

Oh wow, now I have three choices, all of which seem viable. I might pop down to my library and see if any are in stock, although I suspect they only have the Field one...

I've seen that one, Catnap, although it's slightly more than I was hoping to spend. Still, if it's the only book I need then it's worth it. Does it also give details about how to enter data, graphs, etc, or is it purely stats?

Hi all,

I've posted a couple of questions about stats here recently, because I'm somewhat lacking in knowledge of the area. I get a bit of time off this summer, so I was wondering if anyone can recommend any stats books, or books which are good to help novices learn to use SPSS, or perhaps stats and SPSS combined. I don't need a huge knowledge of stats, as I'll probably just use varieties of t-test, ANOVA etc, nothing too complex, but it would still be good to have a working knowledge of these things...

Matt

Can I add more to the question?

In total, I have two independent variables...

1. Concentration of treatment (three groups - 0, 50, and 100).

2. Inhibitor used (none, inhibitor A, inhibitor B)

Each membrane was treated with one of the 9 combinations of concentration/inhibitor, and IL-8 concentration measured above and below the membrane.  Three membranes were performed for each combination of concentration/inhibitor.

All I want to know is whether, for a set combination of treatment/inhibitor, there is a difference between IL-8 above and below the membrane. I'm not interested in looking at IL-8 variation between inhibitors.  I might also like to know whether, for a inhibitor, the IL-8 either above OR below varies significantly with treatment concentration.

From looking online, it seems like I need to do a repeated measures GLM/ANOVA, but can I just do separate paired t tests? And are there any good sites for people with no stats knowledge about how to enter data for these tests and to run them?

Once again, many thanks...

I hate stats :-(

Matt

Update - I'm pretty sure I need a two-sample/independent-sample t-test. But can someone confirm this?

And if so, what about assuming or not assuming equal variances?

Hi all,

I have some data that I want to do statistical analysis on, but I'm not sure which test to do. I'll try to explain in lay terms what I did...

I had some cells, grown on a membrane - 3 sets of cells in total. I gave each of the sets of cells the same treatment, and then determined the level of a chemical called IL-8 both above the membrane, and below the membrane. So I have three results for level of the chemical above the membrane, and three for level of the chemical below the membrane, on set of results per membrane. I want to analyse whether the amount of the chemical above the membrane is different to that below the membrane.

I think I need to use a t-test, but I don't know which sort, so could someone please help? I am pretty crap when it comes to stats, so any advice would be much appreciated :-)

Many, many thanks...

Matt

Maybe I can offer some words of advice here. I have started, and then withdrawn from (I don't like the word "quit"!) a PhD not once, but twice, at different institutions, in successive years. Both withdrawals were on mental health grounds, but were not taken at all lightly. The first was in 2006, and the second in 2007. I started another PhD last September (2009), and all is going well now. When applying for this one, and also the second one, I was completely honest about the past.

My suggestion is first to be open. I think the fact that you're applying for another one shows that it's something you've thought about, and aren't entering into on a whim. It shows that, despite a setback, you still want to do the PhD, which is a very positive thing. As for explaining what happened, perhaps it's not a good idea to say explicitly "it wasn't my fault", but rather just explain what happened, perhaps concisely when you apply, and then more explicity, if prompted, at an interview. If it wasn't your fault, that should be apparent. I also think it helps, as Elsie says, if you have a good academic record, as this shows that you're unlikely to have left because it was beyond you.

If I was a supervisor looking for potential PhD students, I might even be slightly drawn towards one in your position, as it shows you know what you're getting into, and that you've not made a hasty decision.

Good luck, and let us know how you get on!

Matt

Hi Slizor,

My own opinion is that, with the exception of a couple of really, hugely obvious things, which I think virtually everyone would see as plain wrong, being offended is a choice, unconsciously made or otherwise. Whether someone decides to be offended by something or not is generally up to them, and just because someone is offended, it doesn't mean you should necessarily apologise for what you said. Perhaps you could express regret that what you said has offended them, but, unless what you said falls into the "obviously wrong" category, then I don't really see why you should apologise for saying it. Of course, it's hard to say more without knowing what you actually said, but that's my opinion.

What's everyone here doing about the World Cup? Avoiding it like the plague, desperate to see every game, or somewhere in the middle?