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Start with just , and you will be surprised at how much you can .

practice is credited by many people, including
Elizabeth Gilbert, the author of the best selling memoir "Eat Pray Love"
Tim Ferriss, an , and

Consider 3 outcomes of flipping a coin: HEAD, HEAD and HEAD!
This imbalance leads people to behave as if there is a greater chance of a TAILS on the next flip.

This phenomenon of people believing independent games of chance are related, is typically referred to as the 's fallacy.

Another example: On August 18th, 1913 a horde of gamblers in Monte Carlo lost millions as something incredibly unlikely happened. After noticing an ever-increasing number of ‘black’ outcomes on the roulette wheel, people started pushing more and more chips onto ‘red’ convinced that it was due to reverse. Alas, it took 26 spins for the roulette ball to land on red again!

Any other biases examples that affect our lives would you like to share with us?

Note related to ML --- In , it is crucial to avoid this fallacy and recognize that the performance of an algorithm on a particular or in a specific set of circumstances does not guarantee the same performance on new, unseen data. Proper generalization and rigorous evaluation techniques, such as using separate validation or test sets, are necessary to draw valid conclusions about the performance and behaviour of machine learning models.