Essay

IB Theory of Knowledge: Essay Exemplar - Grade A

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Course
IB Theory of Knowledge

Institution
Canterbury College (CC)

In 2020, I received 45 points in IB, including a A in TOK. This essay allowed me to achieve that grade. As TOK has a unique and highly technical form, understanding this essay's style, structure and presentation will help boost your TOK mark considerably. The essay dealt with the Knowledge Quest...

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Uploaded on July 30, 2022
Number of pages 6
Written in 2020/2021
Type Essay
Professor(s) Unknown
Grade A

tok
theory of knowledge
tok essay
grade a
ib
international baccalaureate

Institution
Canterbury College (CC)
Education Unknown
Course
IB Theory of Knowledge

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Word Count (Excluding References): 1588

‘Statistics conceal as much as they reveal.’ Discuss this claim

with reference to two areas of knowledge.

For many people, statistics are understood in black and white. By their numerical nature, the ideas

they assert seem to have little, if any leeway at all. However, as far as statistics may be literally true,

the conclusions they might suggest in a reader’s mind can be far from valid. The concept of

misleading claims has perhaps never been more pertinent as in this current age of information

overload. The title claim suggests that all statistics have an element of mistruth, which ‘conceal’ a

subject’s true nature, and an element of elucidation, which ‘reveal’ it. In this essay, I will investigate

how these elements compare by examining the use of statistics in two Areas of Knowledge (AOK):

mathematics and the human sciences. I intend to argue that our widely flawed grasp of number and

the snapshots statistics give can significantly obscure the truth. However, with the necessary

considerations, I propose that these drawbacks can be mitigated to the extent that statistics reveal

more than they conceal.

The first AOK that I will be investigating is mathematics. To be fully understood, statistics require

the reader to be adequately numerate; after all, statistics are just ways of summarising data

mathematically. Consequently, it could be argued that statistics conceal more than they reveal,

simply because our understanding of mathematics is widely flawed. Compared to the original

understanding of number hard-wired into the brain, our present perception of mathematics is

incredibly unnatural. To demonstrate this, look no further than the Pirahã tribe. The Amazonian

indigenous group uses a counting system comprised only of the words ‘one’, ‘two’ and ‘many’, and

studies show they are unable to complete basic numerical tasks such as comparing quantities

involving numbers greater than 3 [1]. This raises the knowledge question (KQ) of ‘To what extent do

humans have inherent numerical understanding?’

, This suggests that humans lack an innate ability to numerically reason, and so our use of the Way of

Knowing (WOK) of reason to interpret statistics may not be logically coherent. Since most people

are educated, one might propose that this finding is not universally relevant. However, recent studies

indicate that the average person still lacks a well-rounded understanding of figures. For instance,

when the American restaurant A&W introduced the third-pound burger, one focus group study found

the reason sales were underwhelming was that the majority of consumers thought they’d get more

meat buying a quarter-pounder instead [2].

It is not only misunderstandings and fallacious reasoning that impede statistical comprehension,

though; there are deeper inbuilt reactions that are much harder to correct. In HL mathematics, I have

studied Bayes’ theorem, which calculates an event’s probability and is especially relevant in medical

testing. In his TED talk, the statistician Peter Donnelly outlines the following related situation [3]:

Consider a population of 1 million people, where a disease affects 1 in 10,000. A person tests

positive for the disease in a test that is correct 99% of the time. How likely is it that they are

infected? Despite the common impulse to say overwhelmingly likely, Donnelly explains there is a

less than 1% chance; this is due to our propensity to focus on the accuracy of individual tests, rather

than considering how scarce the disease is generally. When confronted by statistics we do not fully

understand, we often use our instinctual intuition to infer their implications. Given that the WOK of

intuition is tied strongly to intangible gut feelings and the WOK of emotion, it is dangerous for it to

influence our conclusions, since they may not be factually substantiated but instead influenced by

personal biases.

In counterclaim, one could argue that statistics are, in and of themselves, truthful. In other words,

statistics do not conceal the truth, but weaknesses in human understanding do. To illustrate this,

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