Probability Distributions and asymmetry

In summary, part c) requires further clarification on whether the uncertainty in the predicted value of A should be taken into account or not.
  • #1
Liquidxlax
322
0

Homework Statement



In a scattering experiment to measure the polarization of an elementary particle, a total of N = 1000 particles was scattered from a target. of these, 670 were observed to scattered to the right and 330 to the left. Assume there is no uncertainty in NL + NR = 1000

a) based on the experimental estimate of the probability, what is the uncertainty in NL and NR?

b) The asymmetry parameter is defined as A = (NL - NR)/(NL + NR). Calculate the asymmetry and its uncertainty

c) Assume that the asymmetry has been predicted to be A= 0.4, find a) and b) with the new value.

Homework Equations



σ = sqrt(Np(1-p) where p is the probability of the outcome and σ is the deviation N is the total number of trials

The Attempt at a Solution



N = 1000 and p = 1/2 so the uncertainty σ in NL and NR is ±15.8

simple plug and chug as well as the asymmetry

which equals 0.34

The problem I'm having is calculating the uncertainty in the asymmetry since the problem states there is no uncertainty in N.

I was thinking that maybe it would be

R + σL)/1000 but then that would mean that the error in the uncertainty would not change.

for part c) the new values of NL = 300 ±15.8 and NR = 700 ± 15.8
 
Physics news on Phys.org
  • #2
so the probability would be 0.3 for NL and 0.7 for NR which would result in an asymmetry of 0.4. The uncertainty in the asymmetry would still be ±0.034 since the error in the uncertainty is not changing with the new values.


Your approach to part a) and b) seems correct. Part c) is a bit confusing because the problem states that the asymmetry has been predicted to be A= 0.4, which means there is no uncertainty in this value. In that case, the calculations for the asymmetry and its uncertainty would remain the same as in part b). However, if you are supposed to take into account the uncertainty in the predicted value of A, you could use the formula for error propagation to calculate the uncertainty in the asymmetry:

σA = √[(∂A/∂NL)^2σNL^2 + (∂A/∂NR)^2σNR^2]

Where ∂A/∂NL and ∂A/∂NR are the partial derivatives of A with respect to NL and NR respectively. These can be calculated using the formula for A given in the problem. This would result in a non-zero uncertainty in A, which would affect the final result for part c).

Alternatively, if you are not expected to take into account the uncertainty in the predicted value of A, then your approach of simply using the new values of NL and NR to calculate the asymmetry and its uncertainty would be correct.
 

FAQ: Probability Distributions and asymmetry

1. What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of a random variable taking on a certain value or range of values. It shows all the possible outcomes of an event and their associated probabilities.

2. What is asymmetry in a probability distribution?

Asymmetry in a probability distribution refers to the unequal distribution of the data points around the mean. This means that one side of the distribution is more spread out than the other, resulting in a non-symmetrical shape.

3. How is asymmetry measured in a probability distribution?

Asymmetry in a probability distribution is measured using skewness, which is a statistical measure that indicates the degree of asymmetry. A positive skewness value indicates a longer right tail and a negative skewness value indicates a longer left tail.

4. What are some examples of asymmetric probability distributions?

Some examples of asymmetric probability distributions include the beta distribution, log-normal distribution, and Weibull distribution. These distributions are commonly used in finance, biology, and engineering, respectively.

5. How does asymmetry affect the interpretation of a probability distribution?

Asymmetry can affect the interpretation of a probability distribution by indicating that the data is not normally distributed and may require different statistical methods for analysis. It can also provide insights into the underlying factors that are influencing the data and help identify potential outliers or extreme values.

Similar threads

Replies
1
Views
2K
Replies
6
Views
1K
Replies
2
Views
2K
Replies
8
Views
2K
Replies
2
Views
2K
Replies
7
Views
1K
Back
Top