In this video we will discuss how to explain and justify your raw data uncertainties.
We will look at how to use the least count value, how at times we might only use a fraction of the
least count value, and we'll consider other reasons for using higher uncertainty values.
Your raw data uncertainties are marked under the analysis criterion.
The least count value is simply the smallest division on the scale of your measuring tool.
For example, on a regular ruler, the smallest division is one millimeter,
so the uncertainty could be plus minus one millimeter, or plus minus 0.1 centimeter.
I use this method in my example I for the frequency. The Google Science Journal app
measures frequency to one decimal place, so the uncertainty in the frequency is plus minus 0.1
hertz. Using a fraction of the least count value means using a fraction, usually half,
of the smallest division on your scale. Using the ruler as an example and using half of the smallest
division, the uncertainty would be 0.5 millimeters or 0.05 centimeter. If I would have used this
technique in my example IA, since the Google Science Journal app measures frequency to one
decimal place, my uncertainty would be health of 0.1 hertz, so 0.05 hertz. Very important,
your values should be given to the same number of decimal places as the uncertainty, so if I would
have chosen this method, then my frequency values should be given to two decimal places like this.
Finally, there might be other reasons for using higher uncertainty values, for example, difficulty
in reading your scale. This can come up in many different ways. In my example IA, I used this
method when measuring the height of the water in the glass. My measuring tape had a one millimeter
scale, but since it was difficult to see the exact height of the water level, I decided to use two
millimeters or 0.2 centimeters as my uncertainty. Please write a paragraph explaining and justifying
your uncertainties. I did this in subsection one under data collection analysis before my raw data
table. As you can see, I explain and justify my uncertainties in both H and F. You don't have to
write a lot here, just make sure that your explanation and justification are clear. This completes
step four, action three. Now you know how to explain and justify your raw data uncertainties.
In the next video, we will discuss how to calculate averages for your dependent variable values.