Systematic vs Random Errors or Quality of Data

In summary, the conversation discusses the effect of a constant current on the resistance of copper wire and a light bulb, and whether this is a systematic or random error in the context of an IA for Ohm's Law. It is concluded that the change in resistance due to temperature is not an error, but potential errors may arise in the measurement process.
  • #1
Cruxxfay
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Both the copper wire and the light bulb became hotter and hotter when a current was constantly passing through them, resulting in an increase in their resistances, which in turn lowering the value of the current through the conductor. (I did not allow time for both the wire and the light bulb to cool down)

The question is, is this a systematic error or random error? Or does this flaw only affect the quality of the data?

I am doing this as my IA and this is part of my evaluation. I have to state which error this flaw causes. I generally think this is a systematic error because it causes an uncertainty in only one direction, but I am not quite sure.
This is an IA for Ohm's Law, to determine whether the copper wire and the light bulb obey Ohm's Law.
 
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  • #2
The fact that resistance changes as a function of temperature is not an error or a flaw. I can assume you made many measurements of voltage and current--how you go about obtaining these data is usually where the errors and flaws creep in.
 

FAQ: Systematic vs Random Errors or Quality of Data

1. What is the difference between systematic and random errors in data?

Systematic errors in data are caused by a consistent bias or mistake in the measurement process, while random errors occur due to chance fluctuations in the measurement. Systematic errors can lead to consistently inaccurate results, while random errors can affect individual data points but typically cancel out when looking at the overall data set.

2. How can I identify whether an error in my data is systematic or random?

To identify whether an error is systematic or random, it is important to look at the pattern of the error. If the error consistently occurs in the same direction and magnitude, it is likely systematic. If the error varies in direction and magnitude, it is likely random.

3. How do systematic and random errors impact the quality of data?

Systematic errors can significantly impact the quality of data by introducing bias and leading to incorrect conclusions. Random errors may affect individual data points but can be minimized by taking multiple measurements. However, if the random errors are too large, they can also reduce the overall quality of data.

4. What measures can be taken to reduce systematic and random errors in data?

To reduce systematic errors, it is important to carefully design and calibrate the measurement process to minimize any potential biases. To reduce random errors, taking multiple measurements and calculating the average can help to cancel out these errors. Additionally, using precise and accurate measurement tools can also help to reduce both types of errors.

5. How can data with systematic and random errors be improved?

Data with systematic errors can be improved by identifying and correcting the source of the error. For example, if a measurement tool is consistently miscalibrated, it can be recalibrated to reduce the systematic error. Data with random errors can be improved by taking more measurements and calculating the average, which can help to reduce the impact of the random errors on the overall data set.

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