What distribution is shown by this histogram?

In summary, the conversation revolved around a histogram generated from 100,000 data sets in Matlab, each consisting of 80 random Poisson distributed numbers. The p-values for each data set were calculated and stored in a vector, and the question of what distribution the histogram showed was raised. There was a discussion about techniques for recognizing the general distribution of data, such as comparing against a normal distribution and using goodness-of-fit tests. The possibility of assuming a specific probability distribution for a statistic was also mentioned.
  • #1
Nyasha
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0
So l generated a 100 000 data sets in Matlab each consisting of 80 random poisson distributed numbers. I then calculated the p-values for each data set and stored them into a vector which l used to make this histogram. I was wondering, what distribution is shown by this histogram
 

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  • #2
Hey Nyasha.

Can you outline what algorithm or transformation of the 80 Poisson distributed simulator calculated results you did?
 
  • #3
Just curious: what type of general techniques there are to recognize the distribution

of data? I know there are ways of comparing against a normal,but not of a way of

deciding the general distribution of some data. And yes, I know there are

goodness-of-fit tests, but this is more an after-the-fact approach.
 
  • #4
if Nyasha can calculate a "p-value" then obviously he must have assumed some specific probability distribution for some statistic. If his assumption is correct, the empirical distribution he had should approximate a "probability of a probability". For example if a random variable X is normally distributed then one may define another random variable Y as: Y = the probability of the value of X that was observed.
 
  • #5
?

Based on the information provided, it appears that the distribution shown by this histogram is a Poisson distribution. This is because the data sets were generated using a Poisson distribution and the p-values were calculated for each set, resulting in a histogram that displays the frequency of these p-values. The shape of the histogram is likely to resemble a bell curve, with the majority of the data clustered around the mean p-value. This is a common characteristic of the Poisson distribution. However, without further information about the data and the specific parameters used in the generation of the data sets, it is not possible to definitively determine the distribution shown by the histogram. Additional analysis and statistical tests may be needed to confirm the distribution.
 

1. What is a distribution?

A distribution is a mathematical function that describes the probability of a random variable taking on a certain value or range of values. It is used to model and analyze data in various fields such as statistics, economics, and physics.

2. How do I determine the distribution of my data?

The type of distribution can be determined by looking at the shape of the data on a graph or by calculating summary statistics such as mean, median, and standard deviation. Additionally, there are various statistical tests that can help identify the distribution of data.

3. What is the difference between a normal and skewed distribution?

A normal distribution is bell-shaped and symmetrical, with the mean, median, and mode all being equal. A skewed distribution, on the other hand, is asymmetrical and has a longer tail on one side. This means that the majority of the values in a skewed distribution tend to cluster towards one end.

4. Can data have more than one distribution?

Yes, data can have multiple distributions if it contains multiple patterns or subgroups. For example, a dataset containing the heights of both men and women would have two distinct distributions - one for each gender.

5. How do I use the distribution of my data in analysis?

The distribution of data is used to make predictions and draw conclusions about a population. It can also be used to calculate probabilities and determine the likelihood of certain outcomes. Additionally, knowing the distribution of data can help in choosing the appropriate statistical methods for analysis.

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