Table of Contents
Tok Essay Titles May 2021
“Statistics conceal as much as they reveal.” Discuss this claim with reference to two areas of knowledge.
The role of Statistics
The purpose centres round how statistics opens up a window to represent data and interpret with a rational decision-making.
It’s pertinent to question here- ‘Statistics are figures, but are all figures statistics?’ When we statistically analyse the performance of an individual student, do we use percentage or percentile?
Comparing similar data sets show deviation from individual values and have ‘reasonable’ standard of accuracy. Does the word ‘reasonable’ indicate partial revelation of data?
Well, at the heart of this claim lies the relative perception on concealment and revelation of data visualization in attaching a certain degree of transparency and reliability quotient to the data.
Concealment
When there is a claim on concealment of facts there must be some realistically hidden parametric variable influence which distort the theoretically mapped relationship between independent or explanatory and dependent or response variable. Think about it!
Do we get to see an expected transparent revelation of size of tomatoes due to the use of fertilizers? What about the impact of sunlight on the response variable?
Let’s consider another real life example. Intensity of damage due to fire would make you dial 911 for a number of fire fighters to address the emergency. This data might not make you convinced with a perfect revelation of the numbers of fire fighters to be called. But does it mean that statistics conceal the transparency in the relationship of these two variates? We might not have considered the size of the fire.
Subjectivity
We have all come across a commonly used claim- ‘Statistics is often intentionally misused’. How do we interpret this assertion? If statistics is left in the hands of an inept researcher to critique, it is misleading and skewed. You just can’t afford to do away with the selection bias of samples.
The right choice of tools
The knee-jerk use of novice tools like line graph or bar graph in natural sciences may show certain errors in observation or lack of transparency as compared to dot or box plots. The curious mind would ask, ‘Why’? I would surely attach a lot of value to the reader’s perception and aptitude in analysing the data set with the correct representation. Also, there can be misinterpretation of data by manipulation with the scale of origin in line graphs.
Let’s analyse the claim zooming through the lens of different AOKs.
Ethical stance in data presentation
Don’t you think unethical behaviour, concealing what has to be revealed and depicting data sets with weak or fake visualizations is a common practice?
Statistics in Arts
Outliers as influencers of sham data visualization are very popularly used in arts. Imagine your weird alien Mr. Bean!
You would get an understanding of how statistical analysis acts as a filter funnel of removing subjectivity.
Rationale
You as a student can only make your analysis and data visualization cogent iff you incorporate the ideas on advanced research to improvise tools, use simulation models, and improvise the static line graph to an interactive one.
Check out for more details on Title 4