Page 176 - Data Science class 10
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3. Write any two practical implementation of Central Limit Theorem?
Ans. Some practical implementations of the Central Limit Theorem include:
• Voting polls estimate the count of people who support a particular election candidate. The results of news channels
that come with confidence intervals are all calculated using the central limit theorem.
• The central limit theorem can also be used to calculate the mean family income for a specific region.
4. Explain the term "availability bias."
Ans. 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.
5. Mention two points that help you recognise about data is bias or not.
Ans. a. Heavily opinionated or one-sided
b. Relies on unsupported or unsubstantiated claims
B. Long answer type questions:
1. Explain the term partiality, preference and prejudice?
Ans. Preference is the act of selecting or having a particular preference for one person or thing rather than another or
others. Prejudice means preconceived opinion that is not based on reason or actual experience. Prejudice is an opinion
held against a person or group that is often unfavourable and/or intolerable and is based on a lack of information.
The difference between prejudice and partiality is that prejudice is a harm, a damage while partiality is a tendency of
preference or bias in favour of one over the other.
2. Explain selection bias with the help of an example. [CBSE Handbook]
Ans. Sample selection bias is a type of bias caused by choosing non-random data for statistical analysis. The bias results
from a sample selection problem when a section of the data is systematically disregarded because of a certain trait.
However, selection bias can be mitigated with the help of various strategies. When the data sample is created, the
sampling strategy should be documented, and any constraints of the procedure ought to be properly expressed. This
documentation will highlight the probability of bias in selection after the model has been developed and used.
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.
As a result, the sample and the target population may differ notably, limiting the purpose of the findings.
3. What is statistical bias?
Ans. Statistical bias is anything that leads to a systematic difference between the true parameters of a population and the
statistics used to estimate those parameters.
For example, a high school student sample will be skewed in a poll to determine teen drug use because it does not
include home-schooled students or dropouts. A sample is also biased if certain members are under-represented or
over-represented relative to others in the population.
Bias is the tendency to favour one thought over another and maybe to ignore competing ideas.
4. Why does the sample size play such an important role in reducing the standard error of the mean? What are the
implications of increasing the sample size?
Ans. The standard error is the standard deviation of the population you are sampling which is divided by the standard
deviation of the sample size. So, mathematically as the sample size increases, the standard error naturally decreases.
But there is more to this, because the standard error is the standard deviation of the population of sample means. So,
as the sample size increases, the sample means are deviating less and less from the true population mean. Hence, as we
sample more, we get statistics which are closer to the true parameters and our inference methods will improve. This is
true for sampling distributions of mean, proportions, and variances.
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