Central Limit Theorem: Fisheries Management

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In summary, the central limit theorem states that under certain conditions, the sample mean of a large number of independent and identically distributed random variables will be normally distributed. This can be applied to fisheries management by considering the age of sampled fish and using the t-distribution if the sample size is small. As the sample size increases, the t-distribution approaches the normal distribution.
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majin
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:rolleyes: just a query... does anyone have a general definition of the central limit theorom. I've been looking on the internet and all I've got is a whole lot of complex crap

P.S it would help if it was related to fisheries management
 
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  • #2
simply put: under certain regularity conditions (finiteness of mean and variance), given Y_i are independent and identically distributed random variables.

[tex]\frac{\sqrt{n}}{\sigma}(\frac{1}{n}\sum_{i=1}^{n}Y_i-\mu) \rightarrow N(0,1)[/tex]

You can think of the [tex]Y_i[/tex] as fish that you are sampling from a population of fish who's mean age and variance you know. Then if you sample a large amount of fish. The average age of your sampled fish standardized as above will be approximately normal(0,1).
 
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  • #3
the one we were taught is that sqrt(n)(X-u)/(s) - >N(0,1) if s>30 otherwise you got to use the t distribution where s is the sample std deviation
 
  • #4
ya so if your n is not large enough, the approximation is not comfortable enough to use the normal. that's fine.
 
  • #5
yeh so as n->infinity the t distrubution tends to the normal
 

FAQ: Central Limit Theorem: Fisheries Management

What is the Central Limit Theorem?

The Central Limit Theorem is a statistical concept that states that when a large number of independent random variables are added together, their sum will tend towards a normal distribution, regardless of the distribution of the individual variables.

How does the Central Limit Theorem apply to fisheries management?

In fisheries management, the Central Limit Theorem can be used to estimate the average size and abundance of a fish population based on a sample of fish caught. This is because the weight or length of each individual fish is considered an independent random variable, and the sum of these variables will tend towards a normal distribution.

What are the assumptions of the Central Limit Theorem?

The Central Limit Theorem relies on three main assumptions: 1) the individual random variables are independent of each other, 2) the sample size is large enough, and 3) the individual variables have finite means and variances.

What are the implications of the Central Limit Theorem for fisheries management?

The implications of the Central Limit Theorem for fisheries management are that it allows for more accurate estimates of fish population size and abundance based on a sample of fish caught. This information can then be used to make informed decisions about sustainable fishing practices.

Are there any limitations to the Central Limit Theorem in the context of fisheries management?

Yes, the Central Limit Theorem may not be applicable in cases where the assumptions are not met, such as when the individual random variables are not independent or the sample size is too small. Additionally, the Central Limit Theorem assumes a normal distribution, which may not accurately represent the distribution of fish populations in certain situations.

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