Comparing Results from an Experiment: What Statistical Measure is Important?

In summary, the software was able to give data about an unknown flame based on the concentrations of OH at different heights above the flame.
  • #1
Saladsamurai
3,020
7
Hi folks :smile:

I have an experiment in which I take an image of a flame. I then run a software routine that tells me what the concentrations of OH (hydroxyl) is at different heights above the flame. I first have to give it a calibrated image of a flame with known data and it then is able to give me data about an unknown flame as stated above. Here is what I have done:

1. Give calibrated the software with a known flame image with known data

2. Imaged 2 different flames that are unknown. the software then returns the OH concentrations of these tow flames.

I am curious to know what statistical measures are important? I suppose I should compare the 2 data sets from the unknown flames so I can show that the data sets are statistically similar and not outside of the bounds of experimental error.

Any thoughts? Please let me know if you need more information.

:smile:
 
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  • #2
Saladsamurai said:
I am curious to know what statistical measures are important?

That is a question for combustion engineering, not for for statistics

My observation of human nature (not yours particularly, but from my sampling of forum postings) is that when people are faced with complicated problems involving probability, they avoid the necessary step of figuring out exactly what they are trying to accomplish. Instead of answering that question, they ask what "statistics" says they should do.

Unfortunately, mathematics and statistics don't tell you anything until you know what you are trying to accomplish. To give some completely hypothetical examples, suppose the flame is a flame in the burner of a furnace. Suppose one consideration for such flames is whether they get hot enough to damage the combustion chamber. Then you would be interested whether hot areas of the flame are near the surface of the chamber, how hot they are, how long this lasts. Perhaps physics tells you that a very tiny hot spot that only lasts for a short time won't burn a pinhole in the chamber, so you would want to know the probability that there would be hot spots that would be big enough and last long enough to be dangerous.

You have to figure out what properties of the flame are important from the viewpoint of combustion engineering.
 
  • #3
Stephen Tashi said:
That is a question for combustion engineering, not for for statistics

My observation of human nature (not yours particularly, but from my sampling of forum postings) is that when people are faced with complicated problems involving probability, they avoid the necessary step of figuring out exactly what they are trying to accomplish. Instead of answering that question, they ask what "statistics" says they should do.

Unfortunately, mathematics and statistics don't tell you anything until you know what you are trying to accomplish. To give some completely hypothetical examples, suppose the flame is a flame in the burner of a furnace. Suppose one consideration for such flames is whether they get hot enough to damage the combustion chamber. Then you would be interested whether hot areas of the flame are near the surface of the chamber, how hot they are, how long this lasts. Perhaps physics tells you that a very tiny hot spot that only lasts for a short time won't burn a pinhole in the chamber, so you would want to know the probability that there would be hot spots that would be big enough and last long enough to be dangerous.

You have to figure out what properties of the flame are important from the viewpoint of combustion engineering.

Hi Stephen :smile:

Thanks for replying. I may have convoluted my intent with my wordiness (as usual). Again, I am specifically looking to:
Me said:
... compare the 2 data sets from the unknown flames so I can show that the data sets are statistically similar and not outside of the bounds of experimental error.

I took a stats course awhile back and I could swear there was a parameter that could help me determine whether the 2 data sets obtained via an experiment under the same conditions are statistically similar, or whether experimental error could be hiding something.

Any thoughts on this?
 

1. What is the purpose of comparing results from an experiment?

The purpose of comparing results from an experiment is to determine if there are any significant differences between the groups being studied. This can help to identify any patterns or trends and provide insights into the relationship between variables.

2. What is the most important statistical measure for comparing results from an experiment?

The most important statistical measure for comparing results from an experiment depends on the type of data being analyzed. Some common measures include mean, median, standard deviation, and p-value. The appropriate measure should be chosen based on the research question and the type of data being collected.

3. How do you interpret the results of a statistical measure when comparing results from an experiment?

The interpretation of a statistical measure depends on the specific measure being used. Generally, a lower p-value indicates a greater likelihood that the results are significant and not due to chance. A higher mean or median may indicate a larger effect size, while a lower standard deviation may indicate a more consistent or predictable relationship between variables.

4. Why is it important to use statistical measures when comparing results from an experiment?

Statistical measures provide a way to objectively analyze and interpret data, rather than relying on subjective judgments. They also allow for comparisons and generalizations to be made about the larger population based on a smaller sample size. Without statistical measures, it would be difficult to determine the significance and validity of the results from an experiment.

5. Can statistical measures be used to draw conclusions about causality in an experiment?

No, statistical measures alone cannot determine causality in an experiment. Causality can only be established through rigorous experimental design and control of all variables. However, statistical measures can provide evidence for relationships between variables, which can then inform further research and investigation into potential causal factors.

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