Comparing real and expected values

  • Thread starter PatternSeeker
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In summary, the conversation discusses a hypothetical problem involving testing participants in three light conditions and measuring their reaching trajectories. The expectation is that the performance in all three conditions will follow a specific trajectory, and the question is how to test this prediction using SPSS.
  • #1
PatternSeeker
19
0
Hi,

Just a hypothetical problem...

Lets say I test participants in three "light" conditions (dim light,
bright light, no light) to see how they reach an object. There are 20 trials
within each condition.

There are four possible reaching trajectories (A, B, C, and D). Let's define them
by a particular angle of a reaching arm to the frontal plane of a participant.

I expect that performance in any of the
three "light" conditions will follow a trajectory "C", and not any other trajectory.
That is, angles of a reaching arm should correspond to angle of trajectory "C"

What would be the most suitable test of this prediction? How could I conduct such
test in SPSS?

Thanks for your help!
 
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  • #2
It isn't clear whether you intend "expected values" to have the standard definition used in probability theory. The expected value of a random variable is its mean (or "average") value. It wouldn't necessarily be the value that a person would expect to observe on every realization of the random variable.
 
  • #3
Hi Stephen,

I will get back to this soon. Thank you.
 

1. What is the difference between real and expected values?

The real value is the actual value that is observed or measured in an experiment or study. It is the value that is obtained from the data collected. The expected value, on the other hand, is the value that is predicted or calculated based on a theoretical model or previous knowledge.

2. Why is it important to compare real and expected values?

Comparing real and expected values allows us to evaluate the accuracy and validity of our predictions or theoretical models. It helps us to identify any discrepancies or errors in our data or assumptions, and to make adjustments or improvements to our methods or theories.

3. How do you calculate expected values?

The method for calculating expected values varies depending on the context and the type of data being analyzed. In general, expected values can be calculated by multiplying the probability of an event by the value associated with that event, and then summing all of these products together.

4. Can real and expected values ever be the same?

Yes, it is possible for real and expected values to be the same. This would occur if the data collected perfectly aligns with the predicted or theoretical values, indicating that the model or assumptions used are accurate and valid.

5. What are some potential sources of error when comparing real and expected values?

There are several potential sources of error when comparing real and expected values. These include measurement error, sampling error, human error, and flaws in the theoretical model or assumptions being used. It is important to carefully consider and address these potential errors in order to accurately interpret the results of the comparison.

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