Confidence intervals for two separate variables?

In summary, the speaker is discussing the results of two experiments, one repeated 40 times and the other using a different method. The results of the second experiment show a lower value and the speaker is trying to determine if there is a statistically significant difference between the two methods. They are looking for a way to show this using mathematical analysis rather than repeating the experiments again. The speaker suggests using the independent two-sample T-test to compare the means of the two experiments. They also mention the importance of statistics for physicists.
  • #1
mikeph
1,235
18
Hi

I have a certain experiment that I repeat 40 times and get the result:

0.001 +/- 0.004.

Now I've repeated the experiment using a different method (so it is essentially a new experiment) and I get a new value:

-0.002 +/- 0.003

Now, is it true to say there is no statistically significant difference between these two different methods? Even though they lie within each other's standard deviation, I think the fact that I've repeated the experiment 40 times should mean something- it makes me confident that method 2 gives a lower result. I don't know how to translate this confidence into statistical analysis though.

The fact that the means are different is clearly not sufficient to convince anyone... how can I convince someone that method 2 gives a lower result? Let's say I even repeat the experiment another 40 times and get EXACTLY the same means and standard deviations. But I know with more certainty the means are different because I've done more experiments. How do I show this using maths though (without actually having to do the experiments?).

Thanks
 
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  • #3
Perfect. Stats really should be compulsory for undergrad physicists!
 

1. What are confidence intervals for two separate variables?

Confidence intervals for two separate variables are a statistical tool used to estimate the range of values within which the true population means of two variables are likely to fall. They are based on sample data and provide a measure of uncertainty around the estimates of the two variables.

2. How are confidence intervals for two separate variables calculated?

Confidence intervals for two separate variables are typically calculated using the sample means and standard deviations of the two variables, as well as the desired level of confidence (usually 95% or 99%). Statistical software or formulas can be used to calculate the intervals, taking into account the sample size and any assumptions about the underlying population distribution.

3. What is the purpose of using confidence intervals for two separate variables?

The purpose of using confidence intervals for two separate variables is to provide a range of plausible values for the true population means of the two variables. This allows for a better understanding of the underlying population and helps to determine if there are significant differences between the two variables.

4. How do I interpret confidence intervals for two separate variables?

Confidence intervals for two separate variables should be interpreted as a range of values within which the true population means of the two variables are likely to fall. The wider the interval, the greater the uncertainty in the estimates. If the intervals for the two variables do not overlap, it suggests that there may be a significant difference between the two populations.

5. What factors can affect the size of confidence intervals for two separate variables?

The size of confidence intervals for two separate variables can be affected by several factors, including the sample size, the level of confidence chosen, and the variability of the data. A larger sample size and a lower confidence level will result in smaller intervals, while a greater variability in the data will result in wider intervals.

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