That's not an answerable question. Sometimes one will be closer, sometimes the other. What you can ask is which produces an unbiased result, i.e. no consistent tendency to underestimate or overestimate.aaaa202 said:I just want to know which is closer to the correct result, i.e. the parameter for the true distribution.
A Poisson distribution is a probability distribution that is used to model the number of occurrences of an event in a given time or space interval. It is often used for count data, such as the number of customers in a store or the number of accidents on a highway.
The mean of a Poisson distribution can be estimated by taking the average of the observed counts in a data set. This is also known as the sample mean. The larger the sample size, the more accurate the estimate of the mean will be.
In a Poisson distribution, the mean and variance are equal. This means that the variance of the data is equal to the mean, which is also known as the parameter lambda. This property makes the Poisson distribution useful for modeling count data.
The mean of a Poisson distribution can be used to make predictions about the number of occurrences of an event in a given time or space interval. For example, if the mean number of accidents on a highway is 5 per day, we can use this information to predict the number of accidents that will occur in a week or a month.
The Poisson distribution is commonly used in many fields, including insurance, finance, and healthcare. It is used to model rare events, such as the number of insurance claims in a year or the number of hospital admissions in a month. It is also used in quality control to monitor defects in a production process.