Logical Reasoning

PT152 · S2 · Q17 Scientist: A number of errors

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Scientist: A number of errors can plague a data-collection process.

Background

Data-collection is plagued by erroneous data points and researchers examine collected data for errors so that they can correct them.

How Can We Explain

Most corrections result in the data point becoming closer to what Jones's theory would predict.

Evaluate

Let's say that Jones's theory predicts a value of 10 for each data point.

We get a bunch of data points: (10, 10, 7, 8, 13, 10, 10, 11, 15, 6, 10, 9)

Some of them turn out to be wrong, and when we fix them, they tend to move closer to Jones's predictions.

For example, we find out that the 7 is an incorrect data point. In reality it should be 8. We find out that the 13 is an incorrect data point. In reality it should be 12.

How come the corrections are shifting closer to Jones's prediction of 10? Shouldn't the errors be random? The 13 was actually lower than it should have been? Or the 7 was higher that it should have been?

What accounts for the fact that corrections usually bring us closer to Jones's estimate?

Goal

My first thought was

However, I know not to get too much anchor bias to my anticipated answer on Paradox questions, since the correct answer is frequently surprising.

17.

Which one of the following, if true, most helps to explain the tendency of corrections in the scientist's field to favor Jones's theory?

Researchers normally give data that
No Impact
We don't care about whether a theory is going to get accepted or not. We just care about why corrected data points are generally drifting towards Jones's theory.
5% picked this
Correct
Researchers in the scientist’s field
Why this is right
This points to a distinction that could be a causal difference-maker. If the people scanning for errors are giving more scrutiny to data points that conflict with Jones's theory, then they won't notice erroneous data points that align with Jones's theory but are actually bad data points. Those data points would have shifted away from Jones's theory. This is more or less describing confirmation bias skewing the results of a study. If a high school teacher thought that male students were more likely to plagiarize than were female students, he might give the essays from male students more scrutiny, in terms of checking online to see if they seemed to have plagiarized any of their papers. Since he doesn't expect the female students to plagiarize, he's not checking their papers as closely. Through this act of confirmation bias, he will reinforce this stereotype he has. He'll find more examples of males plagiarizing than females plagiarizing, since he's putting more effort into looking for trouble with males' papers. Similarly, if these researchers already implicitly assume J's theory is right, they'll have some confirmation bias. They won't bother checking the the data points that align with Jones's theory, because they're assuming those data points are probably correct. Meanwhile, they'll expend effort looking at data points that diverge from Jones's theory, so they'll catch more offenders (erroneous data points).
Skill tested: Paradox · how this choice captures the argument's function is the move to repeat next time.
68% picked this
Researchers in the scientist’s field
Unclear Impact
Someone might have liked this if they were looking for an answer that said, "the corrected data points shift towards Jones's theory, because Jones's theory is right". This is more like, "Some lines of research will favor Jones's theory. These researchers accept Jones's theory, so they pursue flattering lines of research. Thus, since Jones's theory is 'right' for these lines of research, corrections will usually bring data points closer to Jones's predictions." There's a lot of stretching for us to try to turn this answer into that story. The biggest gap is that we have no idea whether any researchers in this field favor Jones's theory, and without knowing that we can't possibly get this answer to go anywhere.
18% picked this
Even if researchers fail to
Very Weak
"Even if ... that does not guarantee" is language that shows there is a possibility for at least one thing. Even if they fail to detect errors, it is possible that at least one error exists. "At least one" strength of language (some, sometimes, not all, can, may, might, need not) is almost never correct on Strengthen / Weaken / Paradox. This answer has no power to make a distinction between data points that do / don't align with Jones's predictions.
5% picked this
Researchers in the scientist’s field
No Impact
The fact that Jones's theory is not the only theory doesn't change anything, and it doesn't explain why corrected data points are mainly moving in the direction of aligning more closely with Jones's theory.
4% picked this

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