I CANT INTO SCIENCE, I HAVE THE DUMB.

Thank you, this is EXACTLY what I needed to read this morning as I head in to an interview for a grad school program I'm not sure I love, but feel like I have to do. I can go in eyes open now and decide if it is for me.

Thank you!

I thought the "I HAVE THE DUMB" would go away at some point during grad school, or at least get less frequent. Here I am in the 4th year of my PhD and it's every goddamn day (except the days I don't come into lab... I like those days).

A good trick when you don't know what to right in the section of a paper is to read another paper and to copy the style. ;)

"You can do pretty much whatever the hell you love, and have a career is pretty much whatever the hell you want to study."

That's a pretty retarded statement. Despite every body babbling on about STEM jobs, there are whole scientific professions that have been wiped out in the US (with the total support of our government). I guess "Bossman" didn't tell you that. You would be surprised how little research is being done on HIV these days in the Pharma industry. Sure there's some vaccine work going on but not too much else. I have personally watched hundreds of scientists being thrown out on the street and they are NOT working as scientists any longer. These are my friends and colleagues. THEY COULD NOT FIND A JOB IN THEIR PROFESSION! There aren't any jobs for them in the US (and they don't want to move to China - it's even more backward ass than Oklahoma.) I am glad that you are a (very) young enthusiastic researcher. I was like you once upon a time. I had a job and a career and I loved doing research. Then reality took over. I'm 49 years old and my little company is running out of money. I'm going to lose my job soon and there is no way I'm going to get another job as a medicinal chemist. I know that. Everyone I know is trying to get out of the profession because it's circling the drain. Don't encourage young people to go into science. There's no jobs and there's only so many of those academic positions that open up every year.

I'm not a pessimist. I'm an optimist with experience.

Thanks for the debating.

I liked the "if evolution was false, ..." question. I'd think we'd still be left with the body of evidence that we've observed with evolution and any replacement theory would have to explain what evolution explains. Similar to how although Newtonian physics is wrong in light of relativistic physics, but relativity has to encompass Newtonian observations and theory. If some sort of deity-explanation is supposed to replace evolution, it additionally has to explain why evolutionary theory works so well.

That's a pretty retarded statement.

"Should I be a virologist?"

and

"What do I have to do if I want to be a virologist?"

... are different questions.

I hadn't listened all the way to the end. Ha! "There is no such thing as cats turning into dogs." and "hasn't begun to replicate itself any farther than what you are describing". Kern completely misses the "endogenous" part of ERV. Having the same kind of mistakes in the allegedly different biblical "kinds" is evidence that they are of the same kind. The tree is connected, they are not wholly new and separate macro-creations.

Maybe that's the way to think of it: shared ERVs are strong evidence against the theory of "macrocreationism". To encompass that fact, the alleged deity must be a trickster who purposely blended the macrocreated "kinds" at a microcreation level to make the biblical interpretation look like a lie.

Happens to us all. Then we sit around feeling like failure frauds, Imposter Syndrome and the like. Some days nothing works. But then, later, you get a good day.

I'm sure statistics explains it all. Or a Jokester Universe, in it to make your life hard for the lulz.

Please let us know if you find a good stat metric for showing the obvious overlap of Data 1 and Data 2.

Two things came to mind:
1) Doing a convolution of Data 1 over Data 2.
2) Using some distribution divergence test out of information theory to show that both Data-1 and Data-2 have the same prior.

Not one thing worked.

Pareto's principle definitely applies in research. Actually, in a stronger way: you get 90% of your results from 10% of your work.

Data 1 and Data 2 obviously overlap. OBVIOUSLY. But what is the right stat metric to 'prove' that they are 'statistically similar'?

What's wrong with a t-test? 'The difference between populations 1 and 2 are not statistically significant.'

Pareto's principle definitely applies in research. Actually, in a stronger way: you get 90% of your results from 10% of your work.

So does Sturgeon's Revelation.

"What's wrong with a t-test? 'The difference between populations 1 and 2 are not statistically significant.'"

That would simply show that you are unable to reject the null hypothesis that they (well, just the means for a t-test) are the same. This could be because they are in fact the same, or, importantly, because you just don't have enough data to tell them apart.

Knowing when to say "I quit *for today* and I am going home" is one of the most essential lessons for anyone to learn. For me, I built on my lab manager's axiom: after you break your second piece of glassware you must go home and do nothing important for the rest of the day. The more things break, the more upset you are, the more things you will break.

(I will grant that this is not always possible, if your samples will be ruined by tomorrow, but you can at least ask for some common-sense supervision to make sure you don't throw out the important fraction.)

It may sound trite, but tomorrow is a new day (if you get to sleep) and a new start.

Snap - I've been mentioned by both my first and last name: Integralmath Justicar. lol

How to prove that data set 1 and data set 2 say the same thing? Easy. For a detailed guide, please see Judy Mikovits take-home* notebooks.

My favorite part of the Q&A (that wasn't the 'debate' debate part. Suppose we should call it the Q*oA - question and occasionally answer) was when Kern was asked if Genesis could be falsified. As his temper flared, he finally said the second (out of 2) statements that was (inadvertently) exactly correct: Can it (Genesis that is) be falsified? It's being falsified all the time!

The other thing he said exactly right is that his theology determines his science. He didn't need to say it, but I appreciate the disclaimer nevertheless. =^_^=

*careless grad student/lab assistant not included.

"Do what you love in science, and you will be fine (you won't be rich, but you will be fine)." That's the story I tell to high school students or undergraduates wondering whether they should follow a career in science.

I grew up reading "old-school" SF, and I really wanted to be a "scientist." Some time during my second year of grad school, plugging away at some track fitting code on my VAXstation (yeah, yeah, I *am* that old), it hit me. "Hey! I'm a _scientist_! I'm doing what I always wanted to do."

I've never forgotten that feeling, and it never fails to re-energize me after too many interminable phone meetings with colleagues, or the seventeenth revision of a paper adding another decimal place to the B -> pi l nu branching fraction. I'm a scientist because I _want_to_be_, not because I "have to be."

"Data 1 and Data 2 obviously overlap. OBVIOUSLY. But what is the right stat metric to 'prove' that they are 'statistically similar'?"
Ehm.. Pareidolia and datamining just told me you'd best leave it out

That would simply show that you are unable to reject the null hypothesis that they (well, just the means for a t-test) are the same. This could be because they are in fact the same, or, importantly, because you just don't have enough data to tell them apart.

That problem exists with any test, but there may be other reasons why a t-test is not appropriate, for example if the data are not normally distributed. Not sure what sort of data Abbie needs to compare, but how about a KolmogorovâSmirnov test for two populations?

Well, I have a shitload of data from experiments that no one has done before, so its a matter of 'Is this the best way to present this kind of data?' 'Is this the best statistical analysis of this kind of data?'

But the frustrating one is I have to compare two Sigmoidal curves-- I cant assume anything, like with dose-response. I cant assume 0-100%. When they are plotted on the same graph they are on top of one another... but how do you get the stats to back it up? The answer might be simple, but I have no friggen clue.

Also, everything is coming up Millhouse again yesterday and today so far-- reminded me that science is like heroin. You keep going from your lowest low, just because you need another hit. And man, when you get that hit-- that beats any fuckin cock in the world.

At the risk of sounding retarded and being on the wrong track. A chi squared test of the data and theoretical Sigmoidal curve? Something like dataset 1 chi-squared, dataset 2 chi-squared, and combined dataset 1 & 2 chi-squared.
Maybe thinking about a chi-squared test will point you in the direction of something that will help.

LMAO

science is like heroin. You keep going from your lowest low, just because you need another hit. And man, when you get that hit-- that beats any fuckin cock in the world.

This needs to be the new tag line of your blog.

#17, windy
Thanks for mentioning that. The 'you need more data to see the significance' bogeyman never really goes away, neither does his life partner, the 'you're only seeing significance because of some unknown systematic error' bogeyman.

Also, everything is coming up Millhouse again yesterday and today so far-- reminded me that science is like heroin. You keep going from your lowest low, just because you need another hit. And man, when you get that hit-- that beats any fuckin cock in the world.

I'm stealing this and taking credit for it. Just like Mitt Romney.