Visual Statistics Challenge

How to analyse, present and conclude a range and/or sample of information based on collated data.

STATISTICS MATTER (data collection activity)

Firstly, the group had to brainstorm, collate and edit a set of questions required for this activity. The final list of questions had 5 categories with about 10 questions each resulting in a total of 50+ questions.

The answering system was set up in an unusual way - using the spatial location of the participants to indicate responses rather than filling out a questionnaire. The room was divided in to two areas, each was assigned either a Yes or No answer, the line that separated these zones was neutral territory.

The participants were asked a question which they answered by positioning themselves in their environment – either standing in one of the Yes /No zones or seating themselves along the line in the middle. Each question/repositioning of the participants was documented with a photograph.

Figure 1. DIBM Data Collection

My thoughts

Right off the bat, I would like to say that the activity we used to gather the data isn’t going to tell us a lot about ourselves as individuals, neither is it going to provide us with true information. Whether you look at this ‘social experiment’ from the point of view of an observer or as a participant it is obvious that certain social and psychological forces were at play. Oh - yes, it is a ‘social experiment’ (at least in my opinion) more than it is a data collection activity. Therefore, this is how I am going to refer to this activity from now on.

What would make this experiment even more interesting is, if we had a chance to answer these questions individually (with no external influence) and then in the settings that we used for this brief. Furthermore, the black and white answers don’t give us much information to begin with. The juiciest piece of information we can get is to know one’s motivation or purpose and to get that kind of knowledge we would have to know the ‘why’ behind the answers.

Essentially, I am assuming that with this social experiment we’ll see how much we conform to the ideas of others in order to secure our position in society and which questions create this sheep mentality phenomenon. I believe that whenever we happen to be in a group setting, we are no longer acting as single units, we are more like mirrors. Among those mirrors there might be a few individuals that are louder than others (in a higher social position, or seen as a leader, or highly opinionated/passionate about the topic), who are more self-assured and therefore don’t feel the mirror effect as much, which gives them the ability to sway others in different directions. The bottom line is that no one can have a very strong opinion about everything, but we still must come up with an answer if we are faced with a black&white question. When in reality, we are more often somewhere in the grey zone.

I am not sure if I am reading too much into things, but the mirror/sheep mentality is what informs the behaviour of our society today – to a worrying extent. Any kind of messaging can travel the world in mere seconds and potentially transform beliefs and value systems of millions. This might seem like a huge business opportunity to some, but it is also a potential danger for the humankind.

Just recently I had a very deep conversation with my friend (who also happens to be in the academia), where we discussed the reasons why her students are reluctant to express themselves in any format, even when she offered alternatives (written, video, voice recording, etc.). We can only assume what has caused this kind of behaviour. However, it wouldn’t be too farfetched to connect the dots all the way to the social media boom. It shouldn’t come as a surprise that the younger generation is wary of voicing any kind of opinion when our society actively prosecutes anyone who doesn’t agree with the majority. What is even worse is that we not only disregard anyone who doesn’t comply with our beliefs as of right now, but we go the extra mile to track down evidence from the past. Ignoring the fact that people make mistakes or that their opinions can change or evolve. This would leave me to believe that the youth of today either voluntarily censorships themselves to keep their position in the society or remains silent not to cause any drama.

If anyone dares to speak up then the gatekeeping police is ready to put them back into their place - essentially saying that you are not opressed enough, you are not young enough, you are not old enough, you are not educated enough (feel free to insert any other fitting feature) to be able to talk about these issues. Which leaves me wondering, since when did being human stop being enough to participate in a conversation or to have opinions? Am I not human enough to express myself at all? For what reason are we tolerating this mass censorship? Who is that helping?

We have come a long way; we are actively fighting for human rights, but it is a vicious cycle and if taken to an extreme it comes back to where we started.

We must be careful with the language we use. In fact, I am not even sure if I can address people as humans (or people for that matter). Perhaps, we can all be ‘circles’ or rectangles, triangles or whatever makes you feel good, but I’ll stick to circles if you don’t mind. To borrow from what my dad has taught me – “You dear circle, you do whatever makes you happy as long as you are not hurting others or pushing your beliefs onto me.”

These are just my assumptions... My other assumption is that I won't be able to confirm my theory as the type of questions we used doesn't really lead to that consensus and the documenting format doesn't show the behaviour in action (perhaps a video would have been more useful for that).

  • Data Visualization Resources


- When presenting data follow the structuture of a story:

Plot - essential context

Twist - what is interesting about the data and what it shows?

Ending - what do you want your audience to do?

- "If there is nothing interesting about the data - Don't show the data!"

Deciphering the Data

We have seperated our questions into 5 categories which is quite handy as it can hep us determine the meaning behind the data in a structured way. But then at the same time some of those questions are quite random - pineapple pizza anyone?


  1. Do you care about politics? 6 no , 4 yes

  2. Left (N)/right wing (Y)? 6 left, 4 neutral

  3. Do you support cancel culture? 7 no, 2 neutral, 1 yes

  4. Vaccines? 9 yes, 1 neutral

  5. Covid is a hoax? 10 no

  6. BLM (Y)/ALM as an Anti-BLM Movement (N)? 9 yes, 1 neutral

  7. Brexit? 8 no, 2 neutral

  8. Do you think the voting system is flawed? 10 yes

  9. In person (Y)/postal (N)? 2 no, 8 yes

  10. Immigrant right to vote? 10 yes

  11. Death penalty? 9 yes, 1 neutral

  12. Euthanasia? 1 no, 1 neutral, 8 yes

This set of data might be tempting you to look at the specific questions and answers as there are quite a few hot topics and clashing idealogies. However, what is more interesting to me is finding out how much we are invested in the political "mumbo jumbo".

Having an answer to these questions means that you know enough to form your opinion (or in this context - you know enough to allign yourself with an appropriate answer). So - what about those neutral answers? The grey area? The ones who don't know enough to side with either one of the options? Or perhaps the ones who know enough to recognize that there is no better choice between the two?

Essentially, in this category I would like to highlight how many times we simply didn't choose an answers.


120 answers, out of which 12 are neutral -> that is 10%.

What's the catch? The first question asked us if we care about politics and 40% of us answered "No", and yet the number of neutral answers doesn't reflect that (or at least I would expect a much higner number). Therefore, I would dare to say that, we care to some extent but the traditional political scene isn't very clear to the general public (us). Therefore, we tend to dodge questions or say that we are simply not bothered about politics when in fact there are issues that we care about very deeply.


  1. Introvert (Y)/Extrovert (N)? 4 no, 3 neutral, 3 yes

  2. Impulsive (N) or controlled (Y)? 5 no, 1 neutral, 4 yes

  3. Honest (Y)/dishonest (N)? 1 no, 2 neutral, 7 yes

  4. Angry (Y)/calm (N)? 6 neutral, 4 yes

  5. Healthy (Y)/unhealthy attachment (N)? 1 no, 5 neutral, 4 yes

  6. Tidy (Y) or messy (N)? 1 no, 1 neutral, 8 yes

  7. Do you have a middle name? 5 no, 5 yes

  8. Business (Y) or art (N)? 7 art, 1 neutral, 1 business

  9. Family (Y) or friends (N)? 1 no, 5 neutral, 4 yes

  10. Do you have a sibling? 3 no, 7 yes

  11. Sex with the lights on (Y) or off (N)? 6 no, 3 neutral, 1 yes

This set of data is about our differences, and since this is how these questions are proposed I can only emphasize this divide in the data visualization.

However, this section isn't just about us as individuals but it also highlights our cultural backdrop. A few questions stand out, for example the middle name question results in an even split which perhaps means that in this regard we are more alike than we think. Then there is the one about siblings where the 'only children' are of asian ethnicity which reflects the difference in family dynamics and culture.


Highlight the extremes, differences and similarities.


  1. Do you speak more than two languages? 6 no, 4 yes

  2. Freelance (N) or owning a business (Y)? 1 no, 8 neutral, 1 yes

  3. Individual (Y) or team worker (N)? 5 no, 2 neutral, 3 yes

  4. Leader (Y) or follower (N)? 1 no, 5 neutral, 4 yes

  5. Ideas (Y)/making (N)? 3 no, 3 neutral, 4 yes

  6. Do you believe in the gender pay gap? 10 yes

  7. Are you for (Y) or against (N) women in STEM? 10 yes

  8. Do you believe that workplaces could offer more support to minority groups? 10 yes

  9. Are you organised when it comes to work, e.g. timekeeping? 1 no, 2 neutral, 7 yes

  10. Written (Y) or vocal (N) communication? 2 no, 3 neutral, 5 yes

This category is quite a mash-up. There are 3 questions (2., 3., 4.) which have a similar meaning, but as we can see from the varying numbers in the answer department, there is some nuance to the meaning of each individual one. Furthermore, questions 6., 7., 8. only show that we conform to the norms of society. This result sparks more questions:

Has anyone from the group experienced the gender pay gap?

Who would be against women in STEM and why?

Do we know how workplaces support minority groups?

Question number 10. tells us that only 2 participants are comfortable with verbal communication. But 4 of us speak more than 2 languages - what is the point of speaking multiple languages then? We also have 4 leaders amongst us and in order to lead you have to talk to your subordinates. Lastly, we have 4 participants who claim to be Ideators and again these people have to communicate, to share, to explain and expand their gift.


Higlight the above mentioned observations.


  1. Do you like TikTok? 8 no, 2 neutral

  2. Do you use social media a lot? 1 no, 9 yes

  3. Are you connected with your culture? 4 no, 1 neutral, 5 yes

  4. Pub (Y) or club (N)? 3 no, 5 neutral, 2 yes

  5. Are you tactile? 10 yes

  6. Travel alone (N) or with friends (Y)? 8 neutral, 2 yes

  7. Abstract (Y) or construct (N)? 3 neutral, 7 yes

  8. Are you in a relationship? 4 no, 1 neutral, 4 yes

  9. Do you want children? 4 no, 3 neutral, 3 yes

  10. Do you have pets? 5 no, 5 yes

Even though we have gained some insightful information from this set of data, it does not need a visual exploration or does it?

Of course, we could look at the almost contradicting statements of high social media use and preference for tactility but I don't see that as an anomaly. We are glued to our phones because that is where everything happens (that is what we believe), but we still have the desire to experience life in its raw form - the fact that we are denied the pleasure of 'being' in the real world under current circumstances only intesifies this feeling.

We know we are missing out on life...


  1. Brushing teeth before (Y)/after breakfast (N)? 2 no, 8 yes

  2. Coffee (Y)/ tea (N)? 1 no, 1 neutral, 8 yes

  3. Beer (Y)/wine (N)? 6 no, 4 neutral

  4. Pineapple on pizza? 7 no, 2 neutral, 1 yes

  5. Gym (Y)/yoga (N)? 1 no, 2 neutral, 7 yes

  6. Window open (Y)/closed (N) when you sleep? 5 no, 1 neutral, 4 yes

  7. Shower in the morning (Y)/evening (N)? 6 no, 4 neutral

  8. Smoker/non-smoker? 5 no, 1 neutral, 4 yes

  9. Meat eater (Y) or non-eater (N)? 1 no, 9 yes

  10. Shop online (Y)/in-store (N)? 2 no, 8 neutral

Habits (or preferences) are personal -> to each their own. Therefore, I won't be drawing any conclusions from this set of data.

The only note that I would like to leave here at the end of my data analysis is that on some photographs it is quite interesting to try and read the body language. Perhaps, what stands out the most is that there are a few participants who seem to be more engrossed by their smartphones than the activity itself.

Data Visualization


Although, the questions were not composed in a way that would lead us to a clear conclusion, it definitely highlights the fact that If you want a concrete answer to something in your research then you have to structure the questions well. However, this strategy can be useful - essentially going broader (doing a trial run) helps us to find out what is missing in the questionnaire. This strategy can be used to identify any blind spots early on in the research.

Tips for future projects

Create a Picture Language (like Isotype) so that the context of the data is more easily identifiable.

List of Illustrations

Figure 1. UCA(2020) DIBM Data Collection.[Photo] In possession of: DIBM.


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