Another Devil's Dictionary
AI: A really stupid movie. See Machine Learning.
Algebraist: Apparently, an odd mix of social scientist, astronomer, and farmer.
Applied Statistician: A data analyst who knows the meaning of "sample statistic" but not "random variable." Knows no mathematics, but does know just enough statistics to be bad at it.
Bayes' Rule: In any sufficiently long discussion of statistics with non-statisticians, no matter how basic or unrelated, someone will always suggest that we should consider "the Bayes approach."
Best Fitting Model: The model that I choose to use. Else, the model that the reviewer chooses for me.
Big Data: Depending on your discipline, could refer to a dataset that is: (1) too big to fit in a single, standard-sized Excel window, (2) too big to fit in a single Excel worksheet, (3) too big to fit on your personal laptop, (4) too big to fit on the university's cloud, or (5) too big to currently encode. Mix with "Data Science" for best results.
Causal Inference: What regression becomes when you draw a DAG first, allegedly. See DAG.
Complex Analyst: An algebraist.
DAG: An acronym for "dirty, awful graph."
Data Analyst: Has no idea that, say, sample statistics are random variables, and has no idea what those words mean. But knows how to make occasionally pretty plots of data in at least one of R or Python.
Data Science: An incantation that can be used to acquire funding in any context. Potency increases when paired with the terms "Machine Learning," "Big Data", and "AI".
Data Scientist: What data analysts have learned to call themselves in order to not as quickly bore strangers at social functions.
Ecologist: A biologist that hates the laboratory.
Geometer: "Two dimensions good, four dimensions bad."
Harmonic Analyst: Fourier with a twist of Fefferman.
Machine Learning: Automating the process of bad decision-making.
Mathematical Statistician: Extinct.
Mathematician: Avoid at all costs.
Methodologist: A kind of parasite. Not good enough to be a "Data Analyst." Doesn't know enough about any subject to be a scientist. Has no clinical or technical skills. Occasionally finds employment by impersonating a "Quantitative Social Scientist."
Multilevel Modelling: "It's not a pyramid scheme, it's multilevel modelling."
Number Theorist: Too good at analysis to be an algebraist, but too good at algebra to be an analyst.
Probabilist: A failed analyst.
Proof: In 21st Century parlance, a simulation.
Psychometrician: A psychologist who once took an intro stats course.
Quantitative Ecologist: A broken abacus that once took an intro bio course. Has learned how to interface with R enough to produce AIC values and LOESS curves, but never actually learned what those things are. Writes an endless stream of papers expounding the virtues of AIC; rejects all papers that do not.
Quantitative Social Scientist: A broken abacus that thinks it's a real person. Has learned how to interface with SPSS enough to fool all the social scientists into thinking s/he knows more about statistics than they do.
Real Analyst: 2nd or 3rd year undergraduate who learns Cantor's diagonalization argument for the first time.
Simulation: An anecdote. Often portrayed as a type of empiricism, it is actually a kind of mysticism. The concept of simulation is an interesting example of a modern-day mass delusion gripping multiple disciplines with little to no substantive exchange. Some key symptoms of the delusion include: an obsession with comparing real numbers to the third, fourth, or higher decimal places; a belief that computers are somehow capable of illustrating scenarios that are different than the ones they have been programmed by their users to convey; and an acute distrust and fear of mathematics. See Proof.
Simulation Study: Any collection of one or more simulations.
Statistical Modelling: The study of conditional probability measures.
Statistician: An applied statistician who thinks s/he is good at math.
Statistics: The study of, practice of, and excuse for quantifying uncertainty about what we don't know.
Topologist: Someone who can tell you if the door is open or shut, but not if you can fit through it.
Algebraist: Apparently, an odd mix of social scientist, astronomer, and farmer.
Applied Statistician: A data analyst who knows the meaning of "sample statistic" but not "random variable." Knows no mathematics, but does know just enough statistics to be bad at it.
Bayes' Rule: In any sufficiently long discussion of statistics with non-statisticians, no matter how basic or unrelated, someone will always suggest that we should consider "the Bayes approach."
Best Fitting Model: The model that I choose to use. Else, the model that the reviewer chooses for me.
Big Data: Depending on your discipline, could refer to a dataset that is: (1) too big to fit in a single, standard-sized Excel window, (2) too big to fit in a single Excel worksheet, (3) too big to fit on your personal laptop, (4) too big to fit on the university's cloud, or (5) too big to currently encode. Mix with "Data Science" for best results.
Causal Inference: What regression becomes when you draw a DAG first, allegedly. See DAG.
Complex Analyst: An algebraist.
DAG: An acronym for "dirty, awful graph."
Data Analyst: Has no idea that, say, sample statistics are random variables, and has no idea what those words mean. But knows how to make occasionally pretty plots of data in at least one of R or Python.
Data Science: An incantation that can be used to acquire funding in any context. Potency increases when paired with the terms "Machine Learning," "Big Data", and "AI".
Data Scientist: What data analysts have learned to call themselves in order to not as quickly bore strangers at social functions.
Ecologist: A biologist that hates the laboratory.
Geometer: "Two dimensions good, four dimensions bad."
Harmonic Analyst: Fourier with a twist of Fefferman.
Machine Learning: Automating the process of bad decision-making.
Mathematical Statistician: Extinct.
Mathematician: Avoid at all costs.
Methodologist: A kind of parasite. Not good enough to be a "Data Analyst." Doesn't know enough about any subject to be a scientist. Has no clinical or technical skills. Occasionally finds employment by impersonating a "Quantitative Social Scientist."
Multilevel Modelling: "It's not a pyramid scheme, it's multilevel modelling."
Number Theorist: Too good at analysis to be an algebraist, but too good at algebra to be an analyst.
Probabilist: A failed analyst.
Proof: In 21st Century parlance, a simulation.
Psychometrician: A psychologist who once took an intro stats course.
Quantitative Ecologist: A broken abacus that once took an intro bio course. Has learned how to interface with R enough to produce AIC values and LOESS curves, but never actually learned what those things are. Writes an endless stream of papers expounding the virtues of AIC; rejects all papers that do not.
Quantitative Social Scientist: A broken abacus that thinks it's a real person. Has learned how to interface with SPSS enough to fool all the social scientists into thinking s/he knows more about statistics than they do.
Real Analyst: 2nd or 3rd year undergraduate who learns Cantor's diagonalization argument for the first time.
Simulation: An anecdote. Often portrayed as a type of empiricism, it is actually a kind of mysticism. The concept of simulation is an interesting example of a modern-day mass delusion gripping multiple disciplines with little to no substantive exchange. Some key symptoms of the delusion include: an obsession with comparing real numbers to the third, fourth, or higher decimal places; a belief that computers are somehow capable of illustrating scenarios that are different than the ones they have been programmed by their users to convey; and an acute distrust and fear of mathematics. See Proof.
Simulation Study: Any collection of one or more simulations.
Statistical Modelling: The study of conditional probability measures.
Statistician: An applied statistician who thinks s/he is good at math.
Statistics: The study of, practice of, and excuse for quantifying uncertainty about what we don't know.
Topologist: Someone who can tell you if the door is open or shut, but not if you can fit through it.