Predictions Can Be Wrong
You have been learning how to make great predictions using clues, patterns, and examples. You are getting really good at this! But here is something important to know: even the best predictions are sometimes wrong. And that is completely normal. That is not a failure — it is just how predictions work.
Why Predictions Are Not Promises
A prediction is your best guess based on the information you have. But the world is full of surprises. The weather forecast said sunny — then a surprise storm rolled in from the coast. Your friend always brings a sandwich for lunch — but today her mom packed noodles instead. The video app predicted you would love this cartoon — but you actually found it boring. None of these predictions were silly or careless. They were all based on good clues and patterns. But something unexpected happened that the clues did not show. That is the nature of predictions: they are about probability — how likely something is — not about certainty — knowing for sure.
A prediction is not a promise. Even very good predictions based on great clues can be wrong sometimes, because the world can always surprise us.
Think about a baseball player at bat. The pitcher has thrown mostly fastballs all game. The batter predicts: the next pitch will be a fastball. That is a great prediction based on strong evidence. But the pitcher throws a curveball — something totally unexpected. The batter was wrong this time. Was the prediction bad? No! It was the smart guess based on everything the batter knew. Sometimes smart guesses still miss, and that is okay. The same thing happens with machines. Even after studying millions of examples, a machine will sometimes predict the wrong thing. This is called an error. Good engineers look at errors and use them to make the machine better.
Complete the sentence about predictions and being wrong.
When a prediction is wrong, the best thing to do is: 1. Notice that it was wrong. 2. Think about why the clues might have been incomplete. 3. Use the new information to make better predictions next time. This is how scientists improve. This is how machine learning systems improve. This is how you improve. Getting something wrong and learning from it is one of the most important ways to grow.
If someone says their prediction is always right — be curious! Even the best predictions are wrong sometimes. A good predictor is honest about that.
A weather app predicts sunshine, but it rains. What does this tell us?
When a machine makes a wrong prediction, what should engineers do?
Wrong and Learn
- Think of a prediction you made recently that turned out to be wrong. It can be anything — guessing what was for dinner, predicting the score of a game, expecting it to be hot outside.
- Write down: what was your prediction? What clues did you use?
- Write down: what actually happened?
- Write down: was there a clue you did not have or did not notice?
- Finally: if you could go back, what would you predict now knowing what you know?
- This is exactly how scientists and machine learning engineers improve: they study their wrong predictions to get better.