Phil 183 Midterm 2 Questions And
Correct Answers 2025-2026.
Attrition bias - Answer a selection effect (similar to the survival effect) in which some
patients drop out of a research study or data is lost in some other way that can result in
unreliable evidence
Echo chambers - Answer a metaphor used for the situation when our sources of information
and opinion have all been selected to support our opinions and preferences. This includes our
own selection of media and friends with similar viewpoints but also results from the fact that
social media tailors what we see using an algorithm designed to engage us.
Evidence for H - Answer when a fact is more probable given H than given ~H it constitutes at
least some evidence for H. By the first rule of evidence this means we should increase our
degree of confidence in H at least a tiny bit.
Evidence test - Answer if we are wondering whether a new fact or observation is evidence
for a hypothesis H we can ask whether that fact or observation is more likely given H or given
~H. If the former it's at least some evidence for H. If the latter it's at least some evidence for ~H.
If neither it's independent of H. The formal version of the evidence test is "Is P( E | H ) greater
or less than P( E | ~H )"?
File-drawer effect - Answer this is a selection effect caused by the researchers themselves
who may not even bother to write up and send in a study that is unlikely to be published (viz. a
boring study) but instead might leave it in their file drawers. See the related entry for
publication bias.
Hypothesis - Answer this is any claim under investigation often denoted with the placeholder
letter "H".
Independent of H - Answer see the entry for evidence test.
Media bias - Answer although this term is generally used to refer only to political biases on
the part of media we use it to cover the general bias towards engaging content though this may
manifest in content of special interest to viewers with a certain political orientation or even
outrightly slanted content. The general category of media bias also includes the highly tailored
algorithms of social media.
Publication bias - Answer the tendency for academic books and journals to publish research
that is surprising in some way. A piece of research can do this by providing evidence against
conventional wisdom or providing evidence for a surprising alternative. Meanwhile studies that
support the conventional wisdom or fail to provide support for alternatives can be passed over.
, Selection Effect - Answer a factor that systematically selects which things we can observe.
This can make our evidence unreliable if we are unaware that it's happening.
Selective noticing - Answer when observations that support a hypothesis bring that
hypothesis to mind causing us to notice that they support the hypothesis but observations that
disconfirm that hypothesis do not bring it to mind. The result is that we are more likely to think
about the hypothesis when we are getting evidence for it and fail to think about it when we are
getting evidence against it. So it will seem to us like we are mainly getting evidence for it. This
can happen even if the hypothesis is just something we've considered or heard about—it
needn't be something we antecedently believe. (So selective noticing can happen without
confirmation bias although it seems to be exacerbated when we do antecedently accept the
hypothesis.)
Serial position effect - Answer tendency to remember the very first and last events in a series
(or the first and last parts of an extended event).
Strength factor - Answer a measure of the strength of a piece of evidence namely the result
of dividing P( E | H ) by P( E | ~H ) where we are measuring the strength of the evidence E
provides for H. The higher the strength factor the stronger the evidence provided by E. (A
strength factor less than 1 is also possible when E is less likely given H than ~H, this means it's
evidence against H.) A traditional but arbitrary threshold for "strong evidence" is about a
strength factor of 10.
Strength test - Answer a test of the strength of a piece of evidence. Informally it involves
asking, How much more (or less) likely is this if H is true than if H is false? Formally the question
is, How much greater (or less) is P( E | H ) than P( E | ~H )? Note that we need a comparative
answer to the strength test so we divide P( E | H ) by P( E | ~H ) to give us the strength factor.
Survivor Bias - Answer this is a more specific term for bias arising from an extreme form of
selection effect when there is a process that actually eliminates some potential sources of
information and we only have access to the ones that survive. For example suppose I have met
lots of elderly people who have smoked all their lives and are not sick and so decide that
smoking is not so unsafe. I may be forgetting that the people who smoked and died are not
around for me to meet them.
Base rate - Answer the overall proportion or probability of a feature in general or in the
population at large.
Central tendency - Answer a value meant to summarize a set of observations by reporting
the typical principal or middle point in the distribution of value(s) observed in the dataset.
Measures of central tendency include the arithmetic mean truncated mean median mode and
geometric mean. These are defined in section 6.3. They differ with respect to their resistance to
outliers among other things. For example if we are concerned about something like the average
Correct Answers 2025-2026.
Attrition bias - Answer a selection effect (similar to the survival effect) in which some
patients drop out of a research study or data is lost in some other way that can result in
unreliable evidence
Echo chambers - Answer a metaphor used for the situation when our sources of information
and opinion have all been selected to support our opinions and preferences. This includes our
own selection of media and friends with similar viewpoints but also results from the fact that
social media tailors what we see using an algorithm designed to engage us.
Evidence for H - Answer when a fact is more probable given H than given ~H it constitutes at
least some evidence for H. By the first rule of evidence this means we should increase our
degree of confidence in H at least a tiny bit.
Evidence test - Answer if we are wondering whether a new fact or observation is evidence
for a hypothesis H we can ask whether that fact or observation is more likely given H or given
~H. If the former it's at least some evidence for H. If the latter it's at least some evidence for ~H.
If neither it's independent of H. The formal version of the evidence test is "Is P( E | H ) greater
or less than P( E | ~H )"?
File-drawer effect - Answer this is a selection effect caused by the researchers themselves
who may not even bother to write up and send in a study that is unlikely to be published (viz. a
boring study) but instead might leave it in their file drawers. See the related entry for
publication bias.
Hypothesis - Answer this is any claim under investigation often denoted with the placeholder
letter "H".
Independent of H - Answer see the entry for evidence test.
Media bias - Answer although this term is generally used to refer only to political biases on
the part of media we use it to cover the general bias towards engaging content though this may
manifest in content of special interest to viewers with a certain political orientation or even
outrightly slanted content. The general category of media bias also includes the highly tailored
algorithms of social media.
Publication bias - Answer the tendency for academic books and journals to publish research
that is surprising in some way. A piece of research can do this by providing evidence against
conventional wisdom or providing evidence for a surprising alternative. Meanwhile studies that
support the conventional wisdom or fail to provide support for alternatives can be passed over.
, Selection Effect - Answer a factor that systematically selects which things we can observe.
This can make our evidence unreliable if we are unaware that it's happening.
Selective noticing - Answer when observations that support a hypothesis bring that
hypothesis to mind causing us to notice that they support the hypothesis but observations that
disconfirm that hypothesis do not bring it to mind. The result is that we are more likely to think
about the hypothesis when we are getting evidence for it and fail to think about it when we are
getting evidence against it. So it will seem to us like we are mainly getting evidence for it. This
can happen even if the hypothesis is just something we've considered or heard about—it
needn't be something we antecedently believe. (So selective noticing can happen without
confirmation bias although it seems to be exacerbated when we do antecedently accept the
hypothesis.)
Serial position effect - Answer tendency to remember the very first and last events in a series
(or the first and last parts of an extended event).
Strength factor - Answer a measure of the strength of a piece of evidence namely the result
of dividing P( E | H ) by P( E | ~H ) where we are measuring the strength of the evidence E
provides for H. The higher the strength factor the stronger the evidence provided by E. (A
strength factor less than 1 is also possible when E is less likely given H than ~H, this means it's
evidence against H.) A traditional but arbitrary threshold for "strong evidence" is about a
strength factor of 10.
Strength test - Answer a test of the strength of a piece of evidence. Informally it involves
asking, How much more (or less) likely is this if H is true than if H is false? Formally the question
is, How much greater (or less) is P( E | H ) than P( E | ~H )? Note that we need a comparative
answer to the strength test so we divide P( E | H ) by P( E | ~H ) to give us the strength factor.
Survivor Bias - Answer this is a more specific term for bias arising from an extreme form of
selection effect when there is a process that actually eliminates some potential sources of
information and we only have access to the ones that survive. For example suppose I have met
lots of elderly people who have smoked all their lives and are not sick and so decide that
smoking is not so unsafe. I may be forgetting that the people who smoked and died are not
around for me to meet them.
Base rate - Answer the overall proportion or probability of a feature in general or in the
population at large.
Central tendency - Answer a value meant to summarize a set of observations by reporting
the typical principal or middle point in the distribution of value(s) observed in the dataset.
Measures of central tendency include the arithmetic mean truncated mean median mode and
geometric mean. These are defined in section 6.3. They differ with respect to their resistance to
outliers among other things. For example if we are concerned about something like the average
