Why are those two events not independent?

In summary, the conditional probability that the first trial is a success, given 8 successes in 10 trials, is .8.
  • #1
cdux
188
0
Provided I know that after 10 attempts I will get 8 successes with a probability of a success per try being 'p', does that affect the success of the first try?

My first instinct was to say it's independent because each attempt is independent but now I know this is a special case where "I know the future" so to speak hence the first try can be expressed conditionally based on that information after 10 tries.

But what I am asking is, what is the formal way to express that dependency, is it only per definition a vague "the occurrence of those 10 tries affect the 1st" or something more explicit?
 
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  • #2
cdux said:
Provided I know that after 10 attempts I will get 8 successes with a probability of a success per try being 'p', does that affect the success of the first try?
No. What you're describing is some event in which "success" has a probability of .8. This says that if you run a large number of trials, about 80% of them will result in "success", but that doesn't tell you what the outcome will be on any individual trial.

For example, flipping a fair coin should result in heads half the time and tails the other half, so p = .5. This doesn't give you information about an individual coin flip, though.
cdux said:
My first instinct was to say it's independent because each attempt is independent but now I know this is a special case where "I know the future" so to speak hence the first try can be expressed conditionally based on that information after 10 tries.
You don't really know the future, other than to be able to say something about the number of successes in the long run.
cdux said:
But what I am asking is, what is the formal way to express that dependency, is it only per definition a vague "the occurrence of those 10 tries affect the 1st" or something more explicit?
 
  • #3
Mark44 said:
No. What you're describing is some event in which "success" has a probability of .8.
The OP can be read another way. Maybe it has been reported after the fact that 8 of the 10 trials were successful, but we'e not told which. Each try is known to have had an a priori probability of p. Assuming the trials were independent, we can now say the probability that the first trial was successful was .8, regardless of p.
cdux, which is the correct interpretation?
 
  • #4
cdux said:
Provided I know that after 10 attempts I will get 8 successes with a probability of a success per try being 'p', does that affect the success of the first try?

My first instinct was to say it's independent because each attempt is independent but now I know this is a special case where "I know the future" so to speak hence the first try can be expressed conditionally based on that information after 10 tries.

But what I am asking is, what is the formal way to express that dependency, is it only per definition a vague "the occurrence of those 10 tries affect the 1st" or something more explicit?

You need the conditional probability that the first trial is a success, given 8 successes in 10 trials. That is, if##X_1 = 1## is the event that the first outcome is a success (with ##P\{X_1=1\} = p##) and if ##\{S_{10} = 8\}## is the event that the sum of the first 10 outcomes is 8 (the other 2 being 0), you want ##P\{X_1 = 1|S_{10} = 8\},## which you can get via Bayes' formulas.
 
  • #5
Ray Vickson said:
you want ##P\{X_1 = 1|S_{10} = 8\},## which you can get via Bayes' formulas.
but it's much simpler just to ask what is the probability that the first trial is one of the eight successes.
 
  • #6
haruspex said:
but it's much simpler just to ask what is the probability that the first trial is one of the eight successes.

I agree---it is simpler---after one is sure that that is a correct way of viewing the problem. Sometimes, to gain familiarity with a method/result/insight, it helps to start with formalism. (One can come up with somewhat similar scenarios in which that view would be questionable, so the binomial nature of the current problem plays an important role.)
 
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FAQ: Why are those two events not independent?

Why is it important to understand the concept of independence in scientific research?

The concept of independence is crucial in scientific research because it allows us to determine the relationship between two variables. If two events are independent, it means that the occurrence of one event does not affect the likelihood of the other event happening. This is important because it enables scientists to make accurate predictions and draw meaningful conclusions from their research.

What does it mean for two events to be considered independent?

Two events are considered independent if the probability of one event occurring does not change based on the occurrence of the other event. In other words, the outcome of one event has no influence on the outcome of the other event.

How can we determine if two events are independent?

To determine if two events are independent, we can use statistical tools such as correlation coefficients or chi-square tests. If the correlation coefficient is close to zero or the chi-square test results in a high p-value, then we can conclude that the events are independent.

What are some common misconceptions about the concept of independence in scientific research?

One common misconception is that if two events occur together frequently, they must be dependent. However, this is not always the case as coincidences can occur even if events are independent. Another misconception is that independence implies causation, when in reality, two events can be independent but have no causal relationship.

How does understanding the concept of independence improve the validity and reliability of scientific research?

Understanding independence helps to eliminate confounding variables and control for them in experiments, which improves the validity and reliability of the results. It also allows researchers to accurately assess the impact of a specific variable on an outcome, rather than attributing the effect to another unrelated event.

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