Using LOC to hire or fire programmers ignores important contextual factors, just as using test scores of students to hire or fire teachers ignores important contextual factors. What language is being used? How large is the project? Existing code? Platform? Bug count? Does the person contribute to the team in other ways?
In teacher evaluation you also have critically important contextual factors. What course is being taught? Are the students motivated? What's the classroom like? A lot of ELL kids in the class; does the teacher speak their language?
Many people see some amount of value in these imperfect metrics because they a) seek to measure the critical output (programmers are paid to make code and teachers are paid to make kids learn), and b) simple numeric metrics aren't biased by human fallibility. Popularity amongst peers won't have any impact on LOC or student test scores.
The author feels they shouldn't be used as 33% of the overall evaluation, which is the suggestion made by Gates.
In terms the specific points the author made about Gates' evidence in favor of their suggestion of 33%, I think he's basically right. I haven't read the original study and so I'm assuming for sake of discussion that he's conveying it correctly.
Over saturated scatterplots are not very useful, and in the hands of someone with no training or knowledge of basic statistics they may be dangerous, but they're not wrong. A trained consumer of research, seeing a plot like the author's first, will only be able to conclude that the correlation is not perfect. That rules out only a subset, and a subset that's fairly uncommon outside of the physical sciences, of the possible relationships between two variables. Not very useful, but not misleading unless there's an assumption that the number of data points is relatively small (which may be the case in some research contexts).
Averaging points and then taking the fake aggregated points and plotting those is wrong. It's using a tool to show variability in the wrong way. Averaging hides the variability that is supposed to be shown in a scatterplot. Why not average down to 2 points and draw the line then? Because that's wrong.
I'm thankful that one or two other people here understand, but a bit dismayed at the kneejerk groupthink.
In teacher evaluation you also have critically important contextual factors. What course is being taught? Are the students motivated? What's the classroom like? A lot of ELL kids in the class; does the teacher speak their language?
Many people see some amount of value in these imperfect metrics because they a) seek to measure the critical output (programmers are paid to make code and teachers are paid to make kids learn), and b) simple numeric metrics aren't biased by human fallibility. Popularity amongst peers won't have any impact on LOC or student test scores.
The author feels they shouldn't be used as 33% of the overall evaluation, which is the suggestion made by Gates.
In terms the specific points the author made about Gates' evidence in favor of their suggestion of 33%, I think he's basically right. I haven't read the original study and so I'm assuming for sake of discussion that he's conveying it correctly.
Over saturated scatterplots are not very useful, and in the hands of someone with no training or knowledge of basic statistics they may be dangerous, but they're not wrong. A trained consumer of research, seeing a plot like the author's first, will only be able to conclude that the correlation is not perfect. That rules out only a subset, and a subset that's fairly uncommon outside of the physical sciences, of the possible relationships between two variables. Not very useful, but not misleading unless there's an assumption that the number of data points is relatively small (which may be the case in some research contexts).
Averaging points and then taking the fake aggregated points and plotting those is wrong. It's using a tool to show variability in the wrong way. Averaging hides the variability that is supposed to be shown in a scatterplot. Why not average down to 2 points and draw the line then? Because that's wrong.
I'm thankful that one or two other people here understand, but a bit dismayed at the kneejerk groupthink.