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In this video we will discuss how to explain and justify your raw data uncertainties.

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We will look at how to use the least count value, how at times we might only use a fraction of the

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least count value, and we'll consider other reasons for using higher uncertainty values.

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Your raw data uncertainties are marked under the analysis criterion.

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The least count value is simply the smallest division on the scale of your measuring tool.

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For example, on a regular ruler, the smallest division is one millimeter,

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so the uncertainty could be plus minus one millimeter, or plus minus 0.1 centimeter.

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I use this method in my example I for the frequency. The Google Science Journal app

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measures frequency to one decimal place, so the uncertainty in the frequency is plus minus 0.1

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hertz. Using a fraction of the least count value means using a fraction, usually half,

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of the smallest division on your scale. Using the ruler as an example and using half of the smallest

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division, the uncertainty would be 0.5 millimeters or 0.05 centimeter. If I would have used this

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technique in my example IA, since the Google Science Journal app measures frequency to one

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decimal place, my uncertainty would be health of 0.1 hertz, so 0.05 hertz. Very important,

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your values should be given to the same number of decimal places as the uncertainty, so if I would

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have chosen this method, then my frequency values should be given to two decimal places like this.

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Finally, there might be other reasons for using higher uncertainty values, for example, difficulty

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in reading your scale. This can come up in many different ways. In my example IA, I used this

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method when measuring the height of the water in the glass. My measuring tape had a one millimeter

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scale, but since it was difficult to see the exact height of the water level, I decided to use two

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millimeters or 0.2 centimeters as my uncertainty. Please write a paragraph explaining and justifying

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your uncertainties. I did this in subsection one under data collection analysis before my raw data

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table. As you can see, I explain and justify my uncertainties in both H and F. You don't have to

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write a lot here, just make sure that your explanation and justification are clear. This completes

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step four, action three. Now you know how to explain and justify your raw data uncertainties.

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In the next video, we will discuss how to calculate averages for your dependent variable values.