Hans Rosling found in his survey that when given two significantly different countries, the students were unable to determine which country was better in terms of wealth? Their level of success was around 1.2 out of 5. Rosling observes that this is much lower than what a chimpanzee would score, as the chimpanzee would have a success score around 2.5 out of 5, or 50%. Rosling also mentions that the faculty scored a 2.4 out of 5, which was also lower than the chimpanzees. The significance of these results is that people are heavily influenced by events and trends in the past to make decisions and perceptions that do not accurately reflect reality at present time.
There were various changes in culture and society that eventually led to Asia’s economic growth. This type of change was significant because it served as an example of what kinds of things could happen that would positively impact the trajectory of a country’s economic stability and growth.
Rosling discusses how as the GCP per capita increases in an area, the child survival rate increases in that same area.
As time progressed from 1962 to 2003, underdeveloped countries were generally able to improve at a faster rate than more developed countries, leading to a smaller income distribution. While the overall trend was positive, the rate at which underdeveloped countries improved varied.
Rosling points out the unreliability of data and the potential misunderstandings that can occur when determining solutions due to the generalizations of the subject. He discussed how different areas within a specific region could have drastically different numbers and conditions, whereas the data would only reflect that region, giving an inaccurate picture of the reality of the situation. This relates to his previous observation regarding preconceived ideas because Rosling’s explanation of how data can inaccurately portray an area demonstrates how a person can be misled into believing something that is not entirely true.
Rosling’s work with the Gapminder project prioritizes helping ordinary people to understand data in a simple, useful way. By making data science more accessible, people will be able to have clearer understandings and thus be able to create and support more effective solutions for the betterment of human wellbeing.