A peculiar prevalence of p values just below .05
Distributions of p were found to be similar across the different journals. Moreover, p values were much more common immediately below .05 than would be expected based on the number of p values occurring in other ranges…
The present study observed evidence of an overreliance on null-hypothesis significance testing (NHST) in psychological research. NHST may encourage researchers to focus chiefly on achieving a sufficiently low value of p. Consistent with that view, the p value distribution from three well-respected psychology journals was disturbed such that an unusually high number of p values occurred immediately below the threshold for statistical significance.
Fitting an elephant with four parameters
With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.
New statistical model lets patient’s past forecast future ailments
Analyzing medical records from thousands of patients, statisticians have devised a statistical model for predicting what other medical problems a patient might encounter.
Like how Netflix recommends movies and TV shows or how Amazon.com suggests products to buy, the algorithm makes predictions based on what a patient has already experienced as well as the experiences of other patients showing a similar medical history.
“This provides physicians with insights on what might be coming next for a patient, based on experiences of other patients. It also gives a predication that is interpretable by patients,” said Tyler McCormick, an assistant professor of statistics and sociology at the University of Washington.
The algorithm will be published in an upcoming issue of the journal Annals of Applied Statistics. McCormick’s co-authors are Cynthia Rudin, Massachusetts Institute of Technology, and David Madigan, Columbia University.
McCormick said that this is one of the first times that this type of predictive algorithm has been used in a medical setting. What differentiates his model from others, he said, is that it shares information across patients who have similar health problems. This allows for better predictions when details of a patient’s medical history are sparse.
“Medical errors reportedly kill more people than traffic accidents”
The National Institute for Health and Welfare (THL) estimates that medical malpractice imposes costs of about a billion euros a year in Finland.
The THL estimates that between 700 and 1,700 people die each year as a result of medical error. This significantly exceeds the number of traffic fatalities.
About EUR 400 million is spent on additional hospital costs. The THL calculates that half of the additional hospital costs could be eliminated through better instruction.
Twelve P-Value Misconceptions
Misconception #1: If P.05, the null hypothesis has only a 5% chance of being true.
Misconception #2: A nonsignificant difference (eg, P .05) means there is no difference between groups.
Misconception #3: A statistically significant finding is clinically important.
Misconception #4: Studies with P values on opposite sides of .05 are conflicting.
Misconception #5: Studies with the same P value provide the same evidence against the null hypothesis.
Misconception #6: P .05 means that we have observed data that would occur only 5% of the time under the null hypothesis.
Misconception #7: P .05 and P <.05 mean the same thing.
Misconception #8: P values are properly written as inequalities (eg, “P <.02” when P .015)
Misconception #9: P .05 means that if you reject the null hypothesis, the probability of a type I error is only 5%.
Misconception #10: With a P .05 threshold for significance, the chance of a type I error will be 5%.
Misconception #11: You should use a one-sided P value when you don’t care about a result in one direction, or a difference in that direction is impossible.
Misconception #12: A scientific conclusion or treatment policy should be based on whether or not the P value is significant.
Using a variety of identification methods and samples, I find that in most cases private spending falls significantly in response to an increase in government spending. These results imply that the average GDP multiplier lies below unity.
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