Why are random variables needed?

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SUMMARY

Random variables (RVs) are essential in statistics as they map experimental outcomes to real numbers, allowing for a structured approach to probability. A probability mass function (PMF) provides the probability of these numbers occurring. This framework enables the independence of probability distributions from specific experiments, facilitating the analysis of sampling distributions, such as the sample mean and variance. Understanding RVs and PMFs is crucial for interpreting statistical data effectively.

PREREQUISITES
  • Understanding of random variables (RVs)
  • Knowledge of probability mass functions (PMFs)
  • Familiarity with sampling distributions
  • Basic statistics concepts, including mean and variance
NEXT STEPS
  • Study the properties of probability mass functions (PMFs)
  • Learn about sampling distributions and their significance in statistics
  • Explore the relationship between random variables and probability distributions
  • Investigate the Central Limit Theorem and its implications for sample means
USEFUL FOR

Statisticians, data analysts, and students in quantitative fields who seek to deepen their understanding of probability theory and its applications in statistical analysis.

dionysian
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please corret me if i am incorrect in my understanding of a RV,PMF or anything else but as i understand it a random variable simply maps a expirmental outcome to a real number. And a probability mass function simply gives the probability that a number will occur.

Now my question is this: why can't we simply map a expiremental outcome to a probability rather than use a random variable to map from the sample space a to a real number then the map a real number to a probability via the PMF?
 
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dionysian said:
please corret me if i am incorrect in my understanding of a RV,PMF or anything else but as i understand it a random variable simply maps a expirmental outcome to a real number. And a probability mass function simply gives the probability that a number will occur.

Now my question is this: why can't we simply map a expiremental outcome to a probability rather than use a random variable to map from the sample space a to a real number then the map a real number to a probability via the PMF?

a random variable is a function of the experimental outcomes. It is convenient not to redefine your probability distribution in terms of every random variable but to keep a fixed probability distribution and then look at the statistics of different random variables.
 
Ahh this is what i suspected... So its fair to say that the RV helps keeps the pdf independent of each expirment
 
dionysian said:
Ahh this is what i suspected... So its fair to say that the RV helps keeps the pdf independent of each expirment

yes.

Typically in statistics you look at sampling distributions. The sample mean and variance are example of random variables on the sampling distribution. If one knows something about the original distribution - say that is is normally distributed - then you know something about the distribution of the mean and the variance.
 

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