Radioactive Decay: Analyzing 1000 Events at 5% Risk Level

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Homework Help Overview

The discussion revolves around analyzing radioactive decay events, specifically examining the timing between consecutive decays and assessing the validity of a hypothesis regarding the characteristic decay time. Participants are exploring statistical methods to analyze the data collected from 1000 decay events, focusing on the distribution of event times and the implications of observed frequencies.

Discussion Character

  • Exploratory, Assumption checking, Mathematical reasoning

Approaches and Questions Raised

  • Participants are considering various statistical distributions, including normal and Poisson distributions, to model the decay events. Questions are raised about the expected fraction of events exceeding certain time thresholds and the implications of observed data on the hypothesis being tested.

Discussion Status

There is an active exploration of different statistical approaches and interpretations of the data. Some participants are attempting calculations related to the chi-squared statistic and discussing the relationships between observed frequencies. Guidance has been offered regarding the interpretation of the decay process and the implications of the observed percentages.

Contextual Notes

Participants are navigating assumptions about the distribution of decay times and the relationship between different time intervals. There is a recognition that the events are stochastic, and the discussion includes considerations of how to appropriately apply statistical tests given the observed data.

skrat
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Homework Statement


Analyzing 1000 events (each event is one radioactive decay of an unknown sample), we notice that the time between two consecutive events is larger than 1 second in 30% of the cases while in 5% it is longer than 2 seconds. Can we, at 5% risk level deny the hypothesis, that the characteristic decay time is 1 second long?

Homework Equations

The Attempt at a Solution


I really don't know how to begin here. I really don't. I do assume that I will have to use either Student distribution or Chi-squared distribution. But I have no idea how to start and what to do :/

Please help!
 
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Which distribution does a radioactive decay have?
Which fraction of events do you expect to be larger than 1 second, and how likely is it to have 300 out of 1000 then?
 
Events should be normally distributed.
Ammm If I understood your question correctly: 300 events should take longer than 1 second. How likely? Am, I would guess with 95% probability.
 
skrat said:
Events should be normally distributed.
The times of the events? Are you sure?
Ammm If I understood your question correctly: 300 events should take longer than 1 second. How likely? Am, I would guess with 95% probability.
300 is your observed number, I am asking for the prediction. And the 95% is something you will have to compare your result with later.
 
Well radioactive decay is a completely stochastic process, knowing this I would say it is normally distributed around characteristic time. But I guess it isn't. My next option, which is a result of guessing, would be that the number of decays observed over a given time interval obeys Poission statistics (distribution). If that is the case, than I would highly appreciate a two sentenced explanation, if not than I am lost. :/

Again, I thought that since all events are stochastic and since measurements show that in 30% of the consecutive decays the time is longer than 1 second, it is also my prediction that 30% of number of decays over a given time interval will always be longer than 1 second.
 
Do you know the concept of half-life? What does it suggest if there is a fixed time during which half of the remaining particles decay?
 
Ok, I think I understand what I am supposed to do. Please check if this seems to be good.

##N=1000## is the number of decays. Experiment tells us that ##N(1 s<t<2 s)=300## and ##N(t>2 s)=50## therefore ##N(t<1 s)=650##.
Of course $$\frac{dP}{dt}=\frac 1 \tau e^{-\frac t \tau}$$ So the theory for first interval (##t<1s##) predicts $$F_1=\int _0^1\frac 1 \tau e^{-\frac t \tau}=1-e^{-1}=0.6321$$ and similary ##F_2=\int _1^2\frac 2 \tau e^{-\frac t \tau}=e^{-1}-e^{-2}=0.232## and ##F_3=\int _2^\infty\frac 2 \tau e^{-\frac t \tau}=e^{-2}=0.135##.

This now leaves me with $$\chi ^2 =\frac{(650-632)^2}{632}+\frac{(300-232)^2}{232}+\frac{(50-135)^2}{135}=73.9$$ while the data from the tables say that $$\chi ^2(3-1) ^{ 5 \text{%}}=5.9915 $$

So I guess the answer is no?
 
I think the 30% "longer than one second" include the 5% "longer than 2 seconds".

The three values are not uncorrelated (because each event has to be in one of the classes) so you cannot add their ##\chi^2##-contributions like that, but the last term alone is sufficient to draw the same conclusion.
 

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