When does my corner shop become statistically relevent?

  • Thread starter WillQ
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In summary, the person is seeking help in calculating the confidence level for determining what percentage of people who walk past their shop will enter. They are looking into using a "confidence interval" for sampling from a binomial distribution and have provided links for further information.
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WillQ
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Hey all,

Here's a nice Wednesday afternoon brain test for you all (I've been pulling my hair out with this one, because I'm sure it's probably straightforward)

If I've got a shop, let's say, and I decide I want to work out how awesome my shop is at getting customers.

I want to work out my awesomeness fraction (let's call it "walk through rate").

I measure:

X, the amount of people that walk past in a given amount of time.

Y, the amount of people that walk past and decide to enter the shop in that given amount of time.

At what point can I say, with a fair level of accuracy, that "Z% of people who walk past my shop come inside"? Obviously I need to wait for a certain number of people to come in or walk past, but I can't work out how to calculate this confidence level... any help would be massively appreciated!

(I was thinking it'd have something to do with calculating the standard deviation, and measuring that as a percentage of the total... but I'm not sure...)
 
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1. When does my corner shop become statistically relevant?

This question is commonly asked in statistical analysis, especially for businesses. The answer depends on the type of data and the purpose of the analysis. In general, a sample size of at least 30 is considered statistically relevant, but larger sample sizes are preferred for more accurate results.

2. How do I determine the sample size needed for statistical relevance?

The sample size needed for statistical relevance depends on several factors, such as the population size, desired confidence level, and margin of error. There are online calculators and statistical formulas that can help determine the appropriate sample size for a specific analysis.

3. Can a small sample size still be statistically relevant?

Yes, a small sample size can still be statistically relevant if it is representative of the larger population and meets the assumptions of the statistical test being used. However, larger sample sizes generally provide more accurate and reliable results.

4. How does statistical relevance impact decision-making for my corner shop?

Statistical relevance is important in decision-making for businesses because it provides evidence for whether a trend or result is likely to be true for the larger population. It allows businesses to make informed decisions based on data rather than assumptions or intuition.

5. What are some limitations of statistical relevance?

Statistical relevance does not guarantee causation, meaning that just because two variables are statistically significant does not mean that one causes the other. Additionally, statistical relevance may not be applicable to all situations and may not account for all factors that could influence the results.

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