# How can you use normal distribution to approximate the binomial distribution?

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To approximate a binomial distribution using the normal distribution, you can use the central limit theorem. This theorem states that the distribution of the sum (or average) of a large number of independent, identically distributed random variables approaches a normal distribution, regardless of the shape of the original distribution. The binomial distribution can be approximated by a normal distribution when the sample size (n) is large and the probability of success (p) is not too close to 0 or 1.

The mean (μ) of the normal distribution is equal to n * p, and the standard deviation (σ) is equal to √(n * p * (1 - p)). By calculating μ and σ, you can then use the normal distribution to approximate probabilities for the binomial distribution.

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When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

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