Use of statistics in experiment

In summary, the best estimate for the random error σ(X) in a single measurement is given by σ(X)2 ≈ 1/(n-1) * ∑((xi-μ)2) where the sum is over all i. However, this equation pertains to multiple measurements rather than a single measurement. Additionally, it corresponds to the sample variance and may not accurately estimate the value of X's random error for the population as a whole. To predict the value for a new or unknown single data point, the equation should use the sample average of the existing data divided by n.
  • #1
Astudious
61
0
I have seen that "the best estimate for the random error σ(X) in a single measurement is given by

σ(X)2 ≈ 1/(n-1) * ∑((xi-μ)2) where the sum is over all i"

I have two questions about this: firstly, how can this pertain to a "single measurement" if it requires the data from multiple measurements (x1, x2, x3, ... xi)? Secondly, this seems to correspond to the sample variance - wouldn't it be a more accurate estimate of the value of X's random error to convert to the variance of the population of X as a whole?
 
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  • #2
Astudious said:
I have seen that "the best estimate for the random error σ(X) in a single measurement is given by

Where did you see this?

If you look at your equation and plug in n = 1, is the variance defined?
 
  • #3
Vanadium 50 said:
Where did you see this?

If you look at your equation and plug in n = 1, is the variance defined?

http://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation

No, but the variance need not (and perhaps should not) be defined for n=1 - a single measurement by definition cannot have a "spread".
 
  • #4
I don't see where it says anything about the variance determined from a single measurement in that article. Where did you see that?
 
  • #5
If you want to use several data points that you already have to predict what will happen for a new or unknown single data point, that is the equation you should use.

PS, The correct equation uses the sample average of the existing data in place of μ. If some how you know μ, you can use it, but divide by n rather than n-1.
 

Related to Use of statistics in experiment

What is the purpose of using statistics in an experiment?

The purpose of using statistics in an experiment is to analyze and interpret the data collected in a systematic and objective manner. It allows researchers to draw conclusions and make predictions based on the data, and also helps to determine the reliability and significance of the results.

How do statistics help in designing an experiment?

Statistics play a crucial role in designing an experiment by helping researchers determine the sample size, sampling method, and the appropriate statistical tests to use. It also helps in identifying potential confounding variables and controlling them to ensure accurate and reliable results.

What is the difference between descriptive and inferential statistics?

Descriptive statistics are used to summarize and describe the data collected in an experiment, such as measures of central tendency and variability. On the other hand, inferential statistics are used to make inferences and predictions about a larger population based on the data collected from a smaller sample.

How do researchers ensure the validity and reliability of their statistical analyses?

Researchers ensure the validity and reliability of their statistical analyses by carefully designing the experiment and controlling for potential biases and confounding variables. They also use appropriate statistical tests and methods, and conduct multiple analyses to confirm the consistency of the results.

What are some common misconceptions about statistics in experiments?

Some common misconceptions about statistics in experiments include assuming that correlation implies causation, using small sample sizes, and relying solely on p-values for determining significance. It is important to understand the limitations and assumptions of statistical analyses and to interpret the results in the context of the research question.

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