Page 167 - Data Science class 10
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These biases hinder the accuracy of the results. Monitoring these risks permits a Data Scientist to more readily take out
these biases. The resultant superior models improve analytics adoption and enhance value from analytics investment.
3.2.5. Survivor Bias
Also called as survivorship bias, it is based on the concept that we usually tend to twist or distort the datasets by
focusing on successful examples and ignoring the failures. This kind of bias also manifests itself while examining the
competitors. For example, a common saying in English in the United States is: “They don’t make them like they used
to.” It claims that items like furniture, vehicles, or machinery were of superior quality in the past than they are today.
This is an example of survivorship bias since all the examples of older goods are items that have lasted until the
present day, while all the low-quality goods from the past have already broken down. In other words, there are now
just high-quality products from the past, but new products of various kinds are still easily accessible.
Survivorship bias can lead to false conclusions because it focuses only on those elements, people, or things that
have made it past a certain point in the selection process, ignoring those that did not. One way to correct sample
selection bias is to assign weights to misrepresented subgroups in order to statistically correct the bias.
3.3.6. Availability bias
The term "availability bias" describes how data scientists draw conclusions only from current or easily accessible
information. They hold the belief that immediate data is relevant data. This can have perilous consequences as it
can shift a data scientist’s focus away from other data points and solutions. Due to its requirement that you use just
recent data, availability bias restricts how you may use data analytics. To overcome availability bias it is important
to set high standards for critical thinking. Be suspicious of the information that comes to you and make sure that
it passes your test for rigour, breadth and depth, and good management of availability bias.
Activity 2
Letter 1 to editor
Dear Sir/Madam
The decision to build a new terminal at Stansted Airport is a clear indication that this Government has
finally abandoned any pretence of listening to local opinion and is determined to discredit and dismantle
any of the remaining rights that local people have to a say in the future of their environment.
No sensible argument has been put forward for the extension. The real losers in this argument are local people.
It seems that if you choose to live by an airport you then give up the right to have a say in local matters.
The private sector is the only opinion the Government seems to want to listen to. The wishes of local people
are now totally ignored in the planning process.
We all want to see a decent, well-planned travel system but this is not the way to go about it. Everybody
should be involved, not just big business.
Yours faithfully
Letter 2 to editor
Dear Sir/Madam
The announcement that the Stansted extension will go ahead will mean a noise pollution and traffic
nightmare for tens of thousands of people living near and around the airport. It must be stopped before
it is too late.
The new terminal will bring a horrendous increase in traffic. It is estimated that there will be an increase of
over 30 million passengers every year. This is totally unacceptable and we must fight to the death to oppose
it. It is simply dreadful that the people of North Essex be made to suffer for the greed of the rest of the
country. It is disgraceful and totally unnecessary that noise and air pollution should be allowed to grow to
what will be a deadly level.
Yours faithfully
Now reply to following questions in your own words:
1. Which letter do you think is the most biased? Explain your reasons.
2. What is the availability of bias in both the letters?
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