Signup date: 30 May 2008 at 11:23am
Last login: 13 Jul 2017 at 12:15pm
Post count: 1964
Here's the nursing diploma to which I was referring.
At the end of the day only you know how much you currently desire each of those three things.
However...
I sympathise. While my situation isn't quite the same as yours, I share some of the issues.
Sometimes people do a Masters degree to improve their CV after a 2.2.
Have you checked with those offers that they wouldn't take you anyway, given that you were close to a 2.1?
Are you confident that if you took further courses, whatever issues that stopped you getting a 2.1 wouldn't recur?
You may have posted this elsewhere, but have you sought any advice from the uni careers service on your applications. Perhaps your job search could be targetted differently, or your CV tweaked in some way? They may also have some thoughts on interim paths that you might not have considered.
I believe most of the major stats packages that you might use for analysing your data e.g. Stata, R will have a function to do this. But nothing wrong with doing a more manual calculation.
If I've understood correctly in your example above it means that you must test at least 72 samples in order to detect any effect at whatever significance level you specified in order to calculate that number, based on the other assumptions you have specified i.e. estimated effect size. Given that these assumptions may be a bit off, and that things can go wrong it is usually preferable to aim for a higher number than specified to allow for this. For example, in a clinical trial, recruiting more people than the minimum is helpful in case some drop out; in a lab study testing extra samples is useful in case an assay fails or has a degree of uncertainty about the result.
Hope this helps
Well, academia funds far more PhDs than post docs, so immediately there are a whole load of people who complete PhDs who then will not get an academic post. The attrition goes on at every career stage. Meanwhile pharmaceutical companies are not the employers they once were. There are some scientific areas that are growing in terms of jobs, but you have to have the right skill set.
Though compared to the arts/humanities the jobs are *relatively* plentiful in science. But it's not an easy ride, and the concept of a permanent job is not something you get to enjoy for the first 5-10 years post PhD. For all the political bleating about the value of STEM to the economy, the funding infrastructure creates a lot of wasted knowledge and opportunities.
By way of analogy, imagine that only between 5-20% of people who completed PGCEs and NQT years ever got their first fully qualified teaching job. Then imagine that if you didn't get promoted to Head of Department you got the boot. Then imagine that after several years of being a HoD you either need to get promoted to a Deputy Head/Head teacher role, or it was game over, no more teaching career, as it is cheaper to replace an experienced HoD with an NQT, even though they cannot offer the same expertise. That is what academia is like. Science is not as bad as other fields, but it is not great either.
If you're aiming for a lab based experimental PhD then a masters might not be necessary, particularly if you have a decent amount of lab experience from your Bachelors. Doing an MSc is less important in lab based PhDs than in other fields. That said, if you could get onto a 1+3 scheme (where year one is (kind of like) an MRes) that'd be a good way of easing yourself in.
Apart from intellectual stimulation, are there any other motivators here? Are you looking for a career switch or will you return to school teaching? To be honest the latter has a lot more career stability that research science. Be aware that what can seem like stimulating for an 8-10 week project can get tired after 4+ months (I do miss some aspects of my lab days but if I never have to count another plate of cells I'll be happy!). So although there are aspects that are stimulating you may find others to be dull and tiresome. Laboratory PhD topics are also somewhat constrained by the resources and the priorities of the lab, so you might not be able to explore things as much as perhaps you are anticipating.
Not trying to be negative, just trying to highlight a couple of things you may not have realised.
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