Survivorship Bias Unmasked: How Misleading Narratives Shape Our World

I wanted to ask, do you know someone who has played Roulette? Offcourse you know many of them, and there are high chances you have played it too BUT if I ask the same question about Russian Roulette??? 

For those who do not know what Russian Roulette is - it is one of the most dangerous game of chance which involves a loaded revolver and player's head:

• A single bullet is loaded into one of the chambers of a revolver.
• The cylinder is spun to randomize the bullet's position. 
• Participants take turns pointing the gun at their own head and pulling the trigger.
• If the chamber with the bullet is selected when the trigger is pulled, the gun fires, resulting in death.

I am sure you have seen it in some or the other form in a movie. If not, watch this Roblox video: https://www.youtube.com/watch?v=UDt7g87dTm8

Why am I suddenly talking about Russian Roulette? No I dont want to make a movie and use this, I want to talk about Survivorship Bias. But since I have already talked about Russian Roulette, let me try to get a little dramatic;

In the dimly lit room, some data scientists embark on a journey to solve the puzzle surrounding Russian Roulette. Armed with a machine learning model and a relentless curiosity, they set out to answer the burning question: Is Russian Roulette truly as deadly as it seems?

To demystify the game's danger, the data scientist find out 1000 individuals who have, played Russian Roulette and lived to tell the tale. They meticulously collect their stories, hoping to uncover the hidden truth behind this lethal game.

As they are talking to the last few participants, an astonishing revelation emerges: every single one of these survivors has defied the odds. Not a single fatality among them. The machine learning model they've crafted, built upon this data of remarkable resilience, confidently proclaims that Russian Roulette is, without any doubt, 100% safe to play.

But here lies the twist—a bias, invisibly, has crept into the heart of this analysis. A bias that we must explore, dissect, and understand - Survivorship Bias.

Because we are only hearing from the ones who survived, we are not getting the full and true picture about how dangerous the game really is.

We read a lot of content, see a lot of videos, TED talks, inspirational podcasts etc where some true data is presented that challenges our beliefs and makes us think differently and now a lot of Machine Learning models are also learning that as the training data set is biased. This might cost the mankind heavily. The way our Machine Learning models are developed and taught to not get biased will decide if the Artifical Intelligence will prove to be a boon or curse to humanity. 

Let me take an example of how different forms of survivorship and related bias is currently used by corporations, political parties and individuals to favor them, leading to others being exploited, deceived and cheated:

Investment Success Stories: When analyzing investment strategies or funds, survivorship bias occurs when only the successful investments or funds are considered. For instance, if a mutual fund company promotes its top-performing funds while neglecting to mention those that have failed or were merged due to poor performance, potential investors may get a skewed view of the company's track record.

Historical Military Aircraft: During World War II, military analysts studied the bullet hole patterns on returning aircraft to determine where to reinforce them. However, this analysis suffered from survivorship bias because it only considered the aircraft that survived their missions. The planes that didn't make it back were never examined, leading to suboptimal reinforcement decisions.

College Dropouts: Examining the success stories of college dropouts who went on to become successful entrepreneurs like Bill Gates and Steve Jobs might lead some to conclude that dropping out of college is a path to success. However, this overlooks the countless dropouts who did not achieve such success, creating a survivorship bias in the analysis of higher education choices.

Health and Longevity Studies: Studies on longevity that focus only on centenarians or individuals who have lived exceptionally long lives may ignore important risk factors for mortality because they exclude people who died at a younger age. This bias can result in misleading conclusions about factors contributing to longevity.

Stock Market Analyses: In stock market analyses, survivorship bias can occur when historical data only considers the stocks that are still actively traded. Delisted or bankrupt companies are often excluded from the analysis, which can lead to an overestimation of the stock market's historical performance.These examples illustrate how survivorship bias can distort our understanding of various phenomena by focusing on the survivors or successes and ignoring the failures or non-survivors. It's important to be aware of this bias and strive for comprehensive data analysis that includes both the successes and failures to make more informed decisions.

Media Manipulation: Political parties may attempt to influence media narratives and coverage to favor their agenda. This can involve biased reporting, spreading fake news, or using social media to control the narrative.

Identity Politics: Political parties in India have often played on caste, religious, and regional identities to create voter blocs. They may highlight the interests of a particular group while downplaying broader societal concerns. This can lead to a biased view of policy priorities and can divide communities for electoral gains.

Inflammatory Rhetoric: Some parties resort to inflammatory rhetoric and divisive narratives to polarize voters along religious or communal lines. They may use biases and stereotypes to portray certain communities negatively or as a threat to garner support from other sections of the population.

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