Probability over a period of time

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SUMMARY

This discussion centers on calculating the probability of an event occurring over a period of time, specifically extending from one day to seven days. The participants clarify that if events are statistically independent, the probability of at least one occurrence in seven days can be calculated using the formula 1 - q^7, where q is the probability of the event not occurring in one day. The conversation also addresses misconceptions about the equivalence of probabilities in different scenarios, emphasizing that probabilities do not simply add up to one due to the complexity of possible outcomes.

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  • Understanding of basic probability concepts, including independent events
  • Familiarity with probability notation (e.g., p for probability of occurrence, q for probability of non-occurrence)
  • Knowledge of exponential decay in probability calculations
  • Ability to interpret and manipulate mathematical expressions involving probabilities
NEXT STEPS
  • Study the concept of independent events in probability theory
  • Learn about the binomial probability formula and its applications
  • Explore the implications of exponential decay in probability over multiple trials
  • Research the law of total probability and its relevance in complex probability scenarios
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Mathematicians, statisticians, data analysts, and anyone interested in understanding probability calculations over time and the implications of event independence.

cdux
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Say I have the probability of something happening in period of a day. How am I going to tackle finding the probability of it happening in a period 7 days? Statistical independence of events or not.
 
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If they are independent it is easy. Let p = prob(event in one day), q=1-p. Then the prob of not happening in seven days is q^7, so prob happening at least once in 7 days is 1-q^7.
 
does that derive from a union of independent events?
 
cdux said:
does that derive from a union of independent events?
No. q^7 comes from the intersection of independent events.
 
Ok, I think I understand the logic now. ty.
 
mathman said:
If they are independent it is easy. Let p = prob(event in one day), q=1-p. Then the prob of not happening in seven days is q^7, so prob happening at least once in 7 days is 1-q^7.

Then how is (1-0.1)^3 = 0.729
and (1-0.9)^3 = 0.01 (only)?

(assuming an event of 0.1 (and it not happening of 0.9))

shouldn't they be equivalent? (their addition leading to 1)

EDIT: Oh, it may be I guess that the closer to 1 a probability is, the more likely to occur[in subsequent runs] in an exponential fashion, whereas the closer to 0, the less likely in a reversely exponential fashion.
 
Last edited:
I am not sure what you are trying to do. It looks like you are calculating probabilities for these two events: happening every day or never happening at all. Obviously they don't add up to 1.

Note (1-0.9)^3=0.001
 
cdux said:
Then how is (1-0.1)^3 = 0.729
and (1-0.9)^3 = 0.01 (only)?
Actually (1- 0.9)^3= 0.001.

(assuming an event of 0.1 (and it not happening of 0.9))

shouldn't they be equivalent? (their addition leading to 1)

EDIT: Oh, it may be I guess that the closer to 1 a probability is, the more likely to occur[in subsequent runs] in an exponential fashion, whereas the closer to 0, the less likely in a reversely exponential fashion.

No, they are not equivalent (and being "closer to 1" has nothing to do with it).

If you have an event that has a probability of 0.1 of "happening" in a given trial, and so a probability of 0.9 of "not happening", then (1- 0.1)^3 is the probability it does NOT happen in three consecutive trials. (1- 0.9)^3= 0.1^3 is the probability it DOES happen in all three trials. They don't add to 1 because there are many other things that could happen: happen in the first trial but not in either of second or third trials, happen on the first and third trial but not on the second trial, etc. "happen on all trials" and "happen on no trials" do not exhaust all the possible outcomes.
 
I understood what I didn't understand a while after the last post of mine.

Simply: 1-(1-"probability_of_what_we_try_to_see_IF_IT_WILL_HAPPEN in_n_subsequent runs")^n

i.e. if I put 0.01 there I try to see if that will happen in n runs. but if I put its complimentary 0.99, I'll be solving ANOTHER PROBLEM, trying to see if its complimentary will happen in n runs.
 

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