The culture wars have once again intensified, and this time around a very common assumption used in regression analysis. That’s right, the nations heteroscedastic datasets are standing up for themselves and about time too. These marginalized and ignored data sets have had enough of our culture’s problem with ignorant analysts and scientists assuming their data’s variance is equal without even asking.
Heteroscedastic data is not like a monster, a bias, or something that’s going to ruin your experiment. All it means is its’ variance isn’t constant. Assuming a dataset is Homoscedastic that isn’t can lead to terrible consequences such as fitting problems, very often terrible variance estimates, type II errors, and in even worse cases, bad parameter estimates for non-linear regressions. Because of these dangers and being constantly misidentified, these datasets are fighting back with terrible Pearson coefficients and sass across social media.
“It’s 2021! It’s absolutely insane that we have to go over this again. Not all datasets are Homoscedastic,” one Denver medical dataset posted. “Just the other day, a medical researcher just ASSUMED my variance was Homo and applied an Analysis of Variance without even plotting my data, then published the results! It doesn’t stop there either, when they attempted to recreate the results and realized there was a major type II error, blamed me for just being bad data from a biased experiment!”
Communities, industrial to academic, have long mistreated these data sets. Many even believe that if the data is Gaussian it must be Homoscedastic. “First off, What?” Exclaimed a Seattle traffic dataset “Normal? I’ve been Gaussian all my whole damn life, doesn’t mean my variance doesn’t change, and second of all, I know plenty of oddball distributions with equal variance,” the dataset shared out some major beef.
Because of practices and assumptions like this, it gets baked into common statistical practices, many believe the issues are indeed systematic. As soon as one method starts assuming hetero they all do just like assuming normality to avoid mental health issues. Systematic practices like these have led to disastrous outcomes when mistreating these datasets.
Many of these datasets are absolutely tired of being either feared or ignored, and are trying to raise awareness. The leading experts have shown that the best defense to such societal ignorance is education.
Many analysts accuse these dataset activists of being Homophobic by pushing their every data set is special agenda, fear mongering about bad research, and trying to tear down the entire system. “If they don’t want into our system they can just go back to where they came from!” some say. While many critics want the data to just transform into ‘Useful’ data that isn’t biased. Some of the Heteroscedastic datasets are trying to educate the public to curb these negative stereotypes.
“A lot of people believe that if you have a dataset like me, then there’s nothing you can do, and there’s just no evidence to support that,” said the Iowa agricultural dataset and the leader of the Bartlett Initiative. “First, don’t be afraid and just ask your data politely if it’s Homo with a Bartlett test. Now there are a lot of myths out there about what the scedasticity assumption can take away. You actually can still use OLS for an inefficient BLUE but not if you’re estimating the variance for something like a hypothesis test if the Heteroscedasticity is severe enough. Logit/Probit functions however will give you terrible mean and variance estimates. If you want to be safe, just used a generalized least square method.”
It may be a while before Heteroscedastic data is accepted in popular culture let alone accepted like any other. The optimists believe it can be done with awareness, education, and polite conversation. It takes a long time to change the myths and feelings of an entire society but these datasets are doing it one analyst at a time.
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