Hypothesis testing
Hypothesis testing is like playing detectives. You know there is a mystery (the question you want to answer), and you have a guess (your hypothesis) about what happened. For example, you might wonder if eating vegetables makes people healthier. Your guess could be that eating vegetables DOES make people healthier.
Now, you need to find out if your guess is correct. So, you collect evidence (data) by asking people what they eat and measuring their health. This is like interviewing people and looking for clues.
Once you have enough evidence, you compare it with your guess. Is the evidence consistent with your guess, or is it against it? If it's consistent, you can say that your guess is likely to be true. If it's against it, you can say that your guess is probably NOT true.
But wait! How do you know if the evidence is consistent or against your guess? You need to do some math (statistical analysis) to answer this question. You calculate something called a p-value, which tells you how likely it is to get the evidence you've collected if your guess is wrong.
If the p-value is very low (less than 0.05), it means that the evidence is not likely to happen by chance alone, and therefore, it is against your guess. If the p-value is high (more than 0.05), it means that the evidence is likely to happen by chance alone, and therefore, it is consistent with your guess.
Finally, you can make a conclusion based on your analysis. If the evidence is against your guess, you need to come up with a new guess and collect more evidence. If the evidence is consistent, you can say that your guess is supported by data, but you need to be cautious because there might be other explanations for your findings.
In a nutshell, hypothesis testing is like being a detective who uses evidence (data) and math (statistical analysis) to find out if their guess (hypothesis) is true or false.
Now, you need to find out if your guess is correct. So, you collect evidence (data) by asking people what they eat and measuring their health. This is like interviewing people and looking for clues.
Once you have enough evidence, you compare it with your guess. Is the evidence consistent with your guess, or is it against it? If it's consistent, you can say that your guess is likely to be true. If it's against it, you can say that your guess is probably NOT true.
But wait! How do you know if the evidence is consistent or against your guess? You need to do some math (statistical analysis) to answer this question. You calculate something called a p-value, which tells you how likely it is to get the evidence you've collected if your guess is wrong.
If the p-value is very low (less than 0.05), it means that the evidence is not likely to happen by chance alone, and therefore, it is against your guess. If the p-value is high (more than 0.05), it means that the evidence is likely to happen by chance alone, and therefore, it is consistent with your guess.
Finally, you can make a conclusion based on your analysis. If the evidence is against your guess, you need to come up with a new guess and collect more evidence. If the evidence is consistent, you can say that your guess is supported by data, but you need to be cautious because there might be other explanations for your findings.
In a nutshell, hypothesis testing is like being a detective who uses evidence (data) and math (statistical analysis) to find out if their guess (hypothesis) is true or false.
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