Final Exam Flashcards
Post hoc fallacy
A occurred, then B occurred
Therefore A caused B to happen
Appeal to tradition
Believing that something is right or true because it is part of a tradition that is revered or respected, especially when there is a more important principle or issue at risk
Appeal to popular opinion
Lots of people believe it, so it must be true
Faulty analogy
Assuming that because two things are alike in one (usually trivial) respect, they must be alike in some other more important respect
Abusive ad hominem
Attacking a person in an abusive way as a means of discrediting their argument or distracting attention from it (eg. don’t listen to that loser)
Ad hominem: poisoning the well
Preemptively presenting irrelevant information about an individual in an attempt to cause their ideas to be ignored, before they are even stated
False alternatives
Promoting your (usually weak) point of view by presenting it together with an even weaker viewpoint, as if they are the only possibilities, when in reality there are other, better possibilities that have not been mentioned
Wishful thinking
I want it to be true, so it must be true
Straw man fallacy
Misrepresenting an opponent’s position or argument in order to make it sound weak or foolish, and therefore easier to attack
Arguing from ignorance
You can’t prove me wrong, so I’m right
Appeal to irrelevant authority
Attempting to support a claim by appealing to the judgment of:
* Someone who doesn’t have appropriate expertise
* An unidentified authority (e.g., ‘scientists’; the internet)
* An authority who is likely to be biased
Fallacy of the mean
Assuming that a moderate or middle view between two extremes must be the best or right one, simply because it is the middle view
CONSORT - participants & generalizability
What to check:
- Eligibility
There should be a clear description of who was eligible to participate
Often given in the form of ‘inclusion criteria’ and ‘exclusion criteria’
- Baseline data
A table should be provided giving a detailed description of the participants
- Generalizability
Look to see if the conclusions are aimed at a population similar to the participants in the study
CONSORT - outcomes 1 (primary & secondary outcomes)
- Has a primary outcome been identified?
If more than one parameter is measured, a primary outcome should be clearly identified - Are conclusions based on the primary outcome?
Conclusions about effectiveness that are based on another measurement (i.e., a secondary outcome) are not valid - Red flags
Studies with no specific primary outcome, in which many parameters are measured, are likely to mistake false positives for treatment effects:
“We compared treatments x and y”
“Our aim was to determine the effects of treatment x”
CONSORT - outcomes 2 (reporting)
- Are results for any of the stated outcomes missing?
If the authors said they were going to measure five outcomes, they should report results for all five outcomes (no cherry-picking) - Look for confidence intervals and absolute values
Look for confidence intervals (not just p-values) and absolute values (not just percentages) - Within-group comparisons to baseline are invalid
Look to see that means were compared to each other, not to baseline within the same group
CONSORT - sample size calculation
- Do the authors say they did a calculation?
- Do they mention the four variables needed for a sample size calculation?
Look for alpha, power, effect size, and variance as evidence that a calculation was actually performed
CONSORT - allocation concealment
- Indicators that they did it well
Try Ctrl-F for words like ‘allocation’ ‘centralized’, etc.
Look for mention of ‘third-party’ allocation (e.g., a pharmacist, on-line allocation tool) - Did they mention Sequentially Numbered Opaque Sealed Envelopes (SNOSE)?
This is not as secure as third-party allocation, but can be effective if employed properly