This example can be used to explain why the smallest of details can be so important when conducting research. This piece by Dvosrsky summarizes a recently published  article that points out a (possible!) major flaw in pre-clinical cancer research using rats. Namely, lab rats aren't being kept at an ideal rat temperature. This leads to the rats behaving differently than normal to stay warm: They eat more, they burrow more, and their metabolism changes. The researchers go on to explain that there are also plenty of other seemingly innocuous factors that can vary from rat lab to rat lab, like bedding, food, exposure to light, etc. and that these factors may also effect research findings. Why is this so important? Psychology isn't the only field dealing with a replicability crisis: Rat researchers are also experiencing difficulties. Difficulties that may be the result of all of these seemingly tiny differences in lab rats that are used during pre-clinical research. I thin...

In my Honors Statistics course, we have discussion days over the course of a semester. One of the discussion topics involves instances when the media has skewered research results (for another example, see this story about  fitness trackers ,) Jezebel writer Caroline Weinberg   describes a  modest study  that found that people who have at least one previous tattoo experience a boost in their immunity when they get subsequent tattoos, as demonstrated via saliva samples of Immunoglobulin A. This is attributed to the fact that compared to tattoo newbies, tattoo veterans don't experience a cortisol reaction following the tattoo. Small sample size but a pretty big effect. So, as expected, the media exaggerated these effects...but mostly because the researcher's university's marketing department did so first. Various new outlets stated things like  "Sorry, Mom: Getting lots of tattoos could have surprising health benefits"  and  "Getting multip...

This news story demonstrates that research methods are both federally monitored and that best practices can change over time. For a long time, women were not used in large scale pharmaceutical trials. Why did they omit women? They didn't want to accidentally exposed pregnant women to new drugs and because of fears that fluctuations in females hormones over the course of a month would affect research results. Which always makes me think of this scene from Anchorman: But I digress. This has been corrected for and female participants are being included in clinical trials. But many of the animal trials that occur prior to human trials still use mostly male animals. And, again, policies have changed to correct for this. This NPR story details the whole history of this sex bias in research. Part of why this bias has been so detrimental to women is because women report more side effects to drugs than do men. So, by catching such gender differences earlier with animal models, the...

This story is pretty easy to follow. Life expectancy varies by income level . The story becomes a good example for a statistics class because in the interview, the researcher describes a multivariate model. One in which multiple different independent variables (drug use, medical insurance, smoking, income, etc.) could be used to explain the disparity the exists in lifespan between people with different incomes. As such, this story could be used as an example of multivariate regression. And The Third Variable Problem. And why correlation isn't enough. In particular, this part of the interview (between interviewer Ari Shapiro and researcher Gary Burtless) refers to the underlying data as well as the Third Variable Problem as well as the amount to variability that can be assigned to the independent variables he lists). SHAPIRO: Why is this gap growing so quickly between life expectancy of rich and poor people? BURTLESS: We don't know. More affluent Americans tend to engage...