Approximating the Binomial Distribution with a Normal Distribution

Oct 13, 2025 by magicalceylontours.com 96 views

Introduction

In probability theory, the binomial distribution is used to model the number of successes in a fixed number of independent trials, where each trial has a constant probability of success. When a fair coin is flipped a large number of times, the binomial distribution can be approximated by a normal distribution. In this article, we will explore how to find the best approximating normal distribution for the number of heads obtained when a fair coin is flipped 60 times.

The Binomial Distribution

The binomial distribution is a discrete probability distribution that models the number of successes in a fixed number of independent trials, where each trial has a constant probability of success. The probability mass function of the binomial distribution is given by:

P(X = k) = (nCk) * (p^k) * (q^(n-k))

where:

X is the number of successes
n is the number of trials
k is the number of successes
p is the probability of success
q is the probability of failure
nCk is the number of combinations of n items taken k at a time

In the case of a fair coin, the probability of success (p) is 0.5, and the probability of failure (q) is also 0.5.

The Normal Distribution

The normal distribution is a continuous probability distribution that is commonly used to model real-valued random variables. The probability density function of the normal distribution is given by:

f(x) = (1 / (σ * √(2π))) * e^(-(x-μ)2 / (2σ^2))

where:

x is the value of the random variable
μ is the mean of the distribution
σ is the standard deviation of the distribution
e is the base of the natural logarithm

Approximating the Binomial Distribution with a Normal Distribution

When the number of trials (n) is large, the binomial distribution can be approximated by a normal distribution. The mean (μ) and standard deviation (σ) of the normal distribution can be calculated using the following formulas:

μ = n * p σ = √(n * p * q)

In the case of a fair coin, the mean (μ) is 30 (since 60 * 0.5 = 30), and the standard deviation (σ) is √(60 * 0.5 * 0.5) = √(15) ≈ 3.87.

Finding the Best Approximating Normal Distribution

To find the best approximating normal distribution, we need to calculate the mean (μ) and standard deviation (σ) of the normal distribution. We can use the formulas above to calculate these values.

μ = 60 * 0.5 = 30 σ = √(60 * 0.5 * 0.5) = √(15) ≈ 3.87

Conclusion

In conclusion, when a fair coin is flipped 60 times, the binomial distribution can be approximated by a normal distribution. The mean (μ) of the normal distribution is 30, and the standard deviation (σ) is approximately 3.87. This normal distribution provides a good approximation of the binomial distribution for large values of n.

The Best Approximating Normal Distribution

The best approximating normal distribution is:

μ = 30 σ = 3.9

Round to one decimal place if entering a decimal answer below.

The best approximating normal distribution is:

Introduction

In our previous article, we explored how to find the best approximating normal distribution for the number of heads obtained when a fair coin is flipped 60 times. In this article, we will answer some frequently asked questions related to this topic.

Q: What is the probability of getting exactly 30 heads when a fair coin is flipped 60 times?

A: The probability of getting exactly 30 heads when a fair coin is flipped 60 times can be calculated using the binomial distribution. However, since we are approximating the binomial distribution with a normal distribution, we can use the normal distribution to estimate this probability.

P(X = 30) = (1 / (3.87 * √(2π))) * e^(-(30-30)2 / (2 * 3.87^2))

P(X = 30) ≈ 0.158

Q: What is the probability of getting more than 30 heads when a fair coin is flipped 60 times?

A: The probability of getting more than 30 heads when a fair coin is flipped 60 times can be calculated using the normal distribution. We can use the z-score formula to calculate the probability:

z = (X - μ) / σ

where X is the value of interest (in this case, 30.5), μ is the mean (30), and σ is the standard deviation (3.87).

z = (30.5 - 30) / 3.87 z = 0.26 / 3.87 z ≈ 0.067

Using a standard normal distribution table, we can find the probability corresponding to a z-score of 0.067:

P(Z > 0.067) ≈ 0.473

Q: What is the expected value of the number of heads when a fair coin is flipped 60 times?

A: The expected value of the number of heads when a fair coin is flipped 60 times is equal to the mean of the binomial distribution, which is:

E(X) = n * p E(X) = 60 * 0.5 E(X) = 30

Q: What is the standard deviation of the number of heads when a fair coin is flipped 60 times?

A: The standard deviation of the number of heads when a fair coin is flipped 60 times is equal to the square root of the variance of the binomial distribution, which is:

σ = √(n * p * q) σ = √(60 * 0.5 * 0.5) σ ≈ 3.87

Q: Can we use the normal distribution to model the number of tails when a fair coin is flipped 60 times?

A: Yes, we can use the normal distribution to model the number of tails when a fair coin is flipped 60 times. The mean and standard deviation of the normal distribution will be the same as the mean and standard deviation of the normal distribution for the number of heads, which are:

μ = 30 σ = 3.87

However, the probability density function of the normal distribution for the number of tails will be the same as the probability density function of the normal distribution for the number of heads, but with the sign of the x-axis reversed.

Conclusion

In conclusion, we have answered some frequently asked questions related to the number of heads obtained when a fair coin is flipped 60 times. We have used the normal distribution to approximate the binomial distribution and have calculated various probabilities and expected values.