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Freethinker’s 2010 Book Club Update

March 1, 2010 in Books, postdoc by Kris | 2 comments

So we’re coming to the end of February, and I’m still reading Darwin’s Sacred Cause, having rechecked it from the library – twice.  Not able to get as much pleasure reading done as I had planned, especially this semester.  I’m taking three classes (Epidemiology, Biostats II, and Grant Writing), plus developing my own research projects and participating in a faculty development program.  I’m also in the process of prepping for two conferences this Spring, and writing up portions of my dissertation for publication (looks like 4 articles). All great things that I’m thoroughly enjoying, but I still have a couple of chapters to go on January’s book.

Not that I haven’t gotten any reading done.  I have a bit of a commute, so I’ve tapped into the audiobook offerings at my local library, and this month I listened to Emma by Jane Austen, and Full House by Steven Jay Gould.  I wish more of the books for this year’s book club were available in audio format, as it would make better use of my time.

Going forward, I think what I’ll do (at least until summer) is choose two books for the month, and pick one to read.

The picks for March are:

Remarkable Creatures: Epic Adventures in the Search for the Origin of Species

Lives in Science: How Institutions Affect Academic Careers

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Speaking of Spring conferences, the first one is coming up next week.  I’ll be tweeting from the Interdisciplinary Exploration of Migration (#kumigration).

Postdoc Resources

December 11, 2009 in postdoc by Kris | No comments

puzzleTo make the most of the postdoc experience, it’s imperative to take advantage of all the resources available. Being a postdoc is an opportunity.  To receive additional skills and training, develop a career track, and establish a network of colleagues to facilitate your research.

Your institution may have an Office of Postdoctoral Affairs.  This serves as a clearinghouse for information relevant to postdoctoral researchers, providing professional development activities and networking with others at the same stage of career development.  In addition, your organization may be a sustaining member of the National Postdoctoral Association. If they are, you are entitled to a free affiliate membership in the NPA. The National Postdoctoral Association provides advocacy, resource-development, and community for postdocs in the US.

In March, the NPA is hosting an the National Summit on Gender and the Postdoctorate in conjunction with their annual meeting. The purpose of the summit is:

  • To examine the unique challenges faced by women as they seek to make the transition from postdoc to faculty.
  • To consider the key factors influencing postdoc women’s decisions to pursue a career in academic science and engineering.
  • To share promising practices and success stories for retaining postdoc women in the academic pipeline.

The keynote speaker at the annual meeting is Dr. Francis Collins, head of the NIH and former director of the Human Genome Project.

The NPA also has resource page, with information on tax issues, career planning, conflict resolution, and retirement. I’m starting a page of helpful postdoc links, which will be regularly updated as I come across new information.

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Image credit: The “Gold Guys” Blog

Alpha, Beta, and Power

December 3, 2009 in Education, postdoc, statistics by Kris | No comments

BiostatisticsI’ve had a few colleagues ask me if Biostats I was a useful class, given my statistics background in grad school.  It’s a requirement for the master’s degree program I’m pursuing, so I have to take it, but I have found it to be a nice refresher of the Biometry course  I had years ago.  Maybe I just know more about statistics now, so it makes more sense; or maybe it’s just explained better in this course, so I have a better grasp of the material. When I started grad school, statistics felt like Farsi. But not now.

Take Type I and Type II error, for example.  In study design, you have to try to minimize both. Type I error is the probability of rejecting the null hypothesis when it is true. The acceptable Type I error rate is determined by alpha, which is generally fixed at 0.05 or lower in the analysis phase of a study.  Type II error, or beta, is the probability of failing to reject the null hypothesis when the alternative hypothesis is true.  While I understood these concepts empirically, the relationship between them had never been explained.  What I had were random facts, with no framework to pin them on.

The relationship between alpha and beta.

The relationship between alpha and beta.

This plot represents a one-tailed Student’s t-test of the difference in means between two independent samples, both with a sample size of 75  and with alpha set to 0.05. The probability of accepting the null hypothesis is represented by the red line, while the probability of accepting the alternative hypothesis is in blue.  Notice that the null hypothesis distribution is centered at 0, meaning that you’re testing the hypothesis of no difference between means, and that the two distributions overlap.  The area under the red curve which overlaps the blue curve is alpha, the chance of rejecting the null hypothesis when it is true.  The area under the blue curve which overlaps the red curve is beta, the chance of failing to reject the null hypothesis when the alternative hypothesis is true.

Notice also, that you can’t change the value of alpha without affecting the value of beta. Here’s the same t-test with alpha set to 0.01.

Changing alpha affects beta.

Changing alpha affects beta.

Reducing alpha increases the critical value for rejecting the null hypothesis (from t=1.6552 to t=2.3518), thus increasing the likelihood of failing to reject the null when the alternative hypothesis is true. And the rest of the blue curve, which equals 1 – beta?  That’s power, or the probability of rejecting the null hypothesis when the alternative hypothesis is true.

That’s the framework I was missing. The biostatistics course was worth that alone.

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Images generated using G*Power 3.

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