# @stem_antics on Instagram

- **Type:** Video
- **Original URL:** https://www.instagram.com/p/DXevD7tAJRf
- **Gondola URL:** https://gondola.cc/posts/64534626-stem-antics-instagram
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/7155cfbaf4.jpg
- **Posted:** 2026-04-23T16:07:08.000+00:00
- **Account Owner:** Stem Antics (@stem_antics) — https://gondola.cc/stem_antics

## Caption

If something has a 1% chance of success, trying it 100 times does NOT guarantee you’ll succeed.

This is one of the most common misunderstandings in probability.

A 1% success rate means each attempt has a probability of 0.01. But probability doesn’t “accumulate” in a simple linear way where 1% × 100 = 100%.

Each trial is independent (assuming conditions don’t change). The correct way to think about this is:

* Probability of failure in one trial = 0.99
* Probability of failing 100 times in a row = 0.99^100 ≈ 0.366
* So, probability of at least one success = 1 − 0.99^100 ≈ 0.634

That’s about a 63.4% chance of success after 100 tries — not 100%.

Even after 100 attempts, there’s still a 36.6% chance you fail every single time.

This is why:

* Rare events stay rare even with repetition
* “I’m due for a win” is not mathematically valid
* Systems that rely on small probabilities require large sample sizes to stabilize outcomes

In real-world terms: startups, experiments, drug trials, machine learning models, and even viral content all operate in this probabilistic space.

The key takeaway: repetition increases your chances, but it does not eliminate uncertainty.

What probability misconception surprised you the most when you first learned it?

#STEM #Probability #Statistics #MathExplained #DataScience

## Stats

- **Views:** 4,194
- **Likes:** 284
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## Tags

probability, stem, statistics, mathexplained, datascience

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