Page 165 - Data Science class 10
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There are several types of sample selection bias, including pre-screening bias, self-selection bias, exclusion bias,
and observer bias.
Example of Sample Selection Bias
For example, medical studies, that recruit participants directly from clinics, are bound to miss all those who don’t
attend these clinics or seek care during the study.
Researchers and study organizers have the responsibility to ensure that the results of their studies are accurate,
relevant, and do not include any prejudice that might result in incorrect conclusions. One way to do this is to
structure the study based on a method that supports a random sample selection process.
While in principle this may sound simple enough, in reality the researcher will need to exercise caution in order
to avoid sample selection bias. Additionally, the study organizer may be faced with restrictions beyond their
control that make it challenging to realise a random sample. For example, there may be a lack of participants or
inadequate funding for the project.
Ensuring that the sample being examined is randomly chosen, the researcher should identify the various subgroups
within the population. They should then analyse the sample to determine if these subgroups are adequately
represented in the study.
In some cases, the researcher may find that certain subgroups are either over-represented or under-represented
in their study. The researcher can now use bias correcting techniques. One method is to assign weights to the
misrepresented subgroups in order to statistically correct the bias. This weighted average considers the relative
importance of each subgroup and can lead to results that more accurately reflect the study population's actual
demographics.
3.3.2. Linearity Bias
Linearity Bias is the belief that changing one quantity would automatically result in a corresponding change in
another. Unlike Selection Bias, Linearity Bias is a cognitive bias; it's produced not through some statistical process,
but rather via how we mistakenly interpret the world around us.
For example, let us take the case of relationship between fuel efficiency and engine displacement (i.e. engine size)
of automobiles?
Automobile engineering tells us that, as engines become larger, their fuel efficiency decreases. The majority of
people believe that the relationship between engine displacement and fuel economy is a straight line. It is, at first,
but real data tells a more complex story for the full picture, as shown in following figure:
Fuel efficiency vs. Engine size
40 class
Highway fuel efficiency (mpg) 30 2seater
compact
midsize
minivan
pickup
subcompact
suv
20
2 3 4 5 6 7
Engine displaement (L)
Identifying Patterns 163

