Which sample is better for making a prediction? Why?

Sample A: A random sample of 10 customers leaving a store

Sample B: A random sample of 100 customers leaving a store

i think maybe sample a would be more better for just a prediction because it's just 10 people. but then again, sample b would be a more accurate prediction. can someone help me out?

I'd go for the larger sample.

To determine which sample is better for making a prediction, we need to consider two factors: representativeness and sample size.

1. Representativeness: For a sample to be good for prediction, it should accurately represent the population we are interested in. In this case, the population would be all customers leaving the store.

- Sample A: This sample consists of only 10 customers. While it is small, it can still provide some insight into customer behavior and preferences. However, there is a risk that it may not fully represent the diversity and characteristics of all customers leaving the store.
- Sample B: This sample consists of 100 customers, which is much larger than sample A. It has a higher likelihood of representing the population more accurately by including a wider range of customer types, behaviors, preferences, etc.

2. Sample size: A larger sample size generally leads to more reliable predictions, as it reduces the impact of random variations and outliers.

- Sample A: Due to its smaller size, the prediction based on this sample may be more susceptible to random variations and less reliable. It may not capture the full range of customer experiences and may be influenced by outliers.
- Sample B: With a larger sample size, predictions based on this sample are more likely to be reliable and generalize well to the entire population of customers leaving the store. It provides a higher level of confidence in the results.

In summary, while both samples have their advantages, sample B (a random sample of 100 customers) is generally a better choice for making predictions. Its larger sample size increases representativeness and reduces the impact of random variations, resulting in a more reliable prediction.