Is the Normal Distribution a Good Approximation for Binomial Probabilities?

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In summary, the conversation revolved around finding the probability using standard deviation and normal distribution. However, it was determined that an inverse norm problem was more applicable, with a mean of .15 and a standard deviation of .0462. It was also discussed that the normal distribution is a good approximation of a binomial if both pn and qn are greater than 5.
  • #1
StatsCat33
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Homework Statement
According to a survey, only 20% of customers who visited the website on a major retail store made a purchase. Random samples of 75 are selected. 90% of the samples will have less than what percentage of customers who will make a purchase after visiting the website? (Round 2 decimal places)
Relevant Equations
SD = sqrt (p(1-p)/n)
1. I first started by finding the Standard Deviation and got it to be SD = sqrt((.15)(.85)/75) = .0412

2. I then thought it was was a probability they wanted me to find and deal with a normal distribution.
I found the normalcdf (-1000000000,.9,.15..0412) and I got 1. I assumed that was wrong and then stopped.
 
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  • #2
Now that I think about it more, I think this is an inverse norm problem. I think I need to say the mean is .15 the SD = .0462 and then the invNorm of it being less than 90% would be 25.92%
 
  • #3
The standard deviation of a general PDF is not a good way to determine probabilities. A "rule of thumb" is that the normal distribution is a good approximation of a binomial if both pn and qn=(1-p)n are greater than 5 (there is more to it than that which you might want to check). In your case, pn=.2*75=15 and (1-p)*75=60. Approximate it with ##N##(np, np(1-p)). I can't figure out what you used.
 

What is the Normal Distribution?

The Normal Distribution is a probability distribution that is often used to describe the behavior of a large number of random variables. It is characterized by a bell-shaped curve and is symmetric around the mean.

What is the Binomial Distribution?

The Binomial Distribution is a discrete probability distribution that describes the number of successes in a fixed number of independent trials with a constant probability of success. It is often used to model binary outcomes, such as heads or tails in a coin toss.

Why is the Normal Distribution used to approximate Binomial Probabilities?

The Normal Distribution is used to approximate Binomial Probabilities because it is a continuous distribution, while the Binomial Distribution is discrete. This means that the Normal Distribution can provide a more accurate approximation for a large number of trials.

Under what conditions is the Normal Distribution a good approximation for Binomial Probabilities?

The Normal Distribution is a good approximation for Binomial Probabilities when the number of trials is large (n ≥ 30) and the probability of success is not too close to 0 or 1 (0.1 ≤ p ≤ 0.9). Additionally, the sample size should be less than 10% of the population size.

What are the limitations of using the Normal Distribution to approximate Binomial Probabilities?

The Normal Distribution is not a perfect approximation for Binomial Probabilities and can lead to inaccurate results when the number of trials is small or the probability of success is close to 0 or 1. In these cases, it is better to use the Binomial Distribution directly.

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