Self-study
Science writer: All scientists have beliefs and values that might slant their interpretations of the data from which they draw their conclusions. ████████ ███████ ██████████ ██████ ███ █████████ ████████ ██ ████ █████ ██████████ ██████ ████████████ █████ █████████ ███ ██████ ██ ██████ ███ ██████ ██ ██████ ████ ████ ██ ███ ██████ █████ ███ ███████ ███████████████ ██ ██████████ ████ ████ █████████ ████ ████ ███████ ██████ ████████████
Summary
The author concludes that serious scientific papers are generally free of bias. Why? These papers are reviewed by many other scientists before publication, and any shared biases will likely be noticed and removed.
Notable Assumptions
The author assumes that all scientists don’t share the same biases. Since only biases that the reviewers don’t share will be removed, if all scientists share the same biases, then the published papers will contain these biases.
25.
Which one of the following ██ ██ ██████████ ████████ ██ ███ ███████ ████████ █████████
a
The scientists reviewing ███████ ██████████ ██████ ███ ███████████ ██ ███ ██████ ████ ██████ ██████ ██ █████ █████ ███████████████ ██ ███ ████ ██ █████ ███████
Even if each reviewer has certain biases they can’t detect, as long as those biases are not shared by the other reviewers, then they will be removed.
b
In general, biases ████ █████ ███████████████ ██ ████ ██ ███████ ██████████ ██████ █████ ████████ ███ ███████████ ███ ███ ██████ █████ ███ ███████████
If (B) is not true—all scientists share the same biases—then no scientists will be able to detect and remove them. As a result, all scientific papers would share these biases, and the conclusion would fall apart.
c
Biases that are ███████ ██ █████████ ██████████ ██████ ███ ██████ ██ ████ ███████████ █████████ █████ ███ ██████ ███ ███████ ███ ████████ ██ ██████ ███ ██████████ █████ ██ █████ ███████
The argument is unconcerned with the scientific value of these papers. We care about whether or not these papers are biased, not if any present bias affects their value.
d
The interpretation of ████ ██ ███ ████ ████ ██ █ ███████ ██████████ █████ ████ ██ █████████ ███████ ██ ███ ███████ ███ ██████ ██ ███████████
The premises list data interpretation as one potential area for bias, but they also tell us that any bias overall can be removed by the reviewers, provided they don’t share it. Even if some other aspect of the paper is biased, it could still be detected, so this answer choice is not necessary.
e
Slanted interpretations of ████ ██ █ ██████████ █████ ███ ██ ███████ ████ ███████ ███████ ██████ ██ ██████████ ███ ██ ███ █████ ███ ██████ ██ ███ ██████ ██ ███████ ██ ███ ██████
The premises give us review as one method of detecting bias, but it doesn’t have to be the only method. In fact, negating (E) to say, “biased data can be removed by means other than review by scientists who do not share the same biases,” supports the conclusion that scientific papers are unbiased, so it cannot be our answer.