Even in a perfect world, measured quantities normally varies and one goal of physics is to detect real variation on top of normal "noise".
There are precise quantitative ways to do this but it requires more background in statistical methods. In this course, we will mostly use visual tools to make decisions about data.
For lab 1, we looked at small vs big push of the IOLab. We used the boxplot feature in StatKey to visualize data. The boxplot gave us a very nice visual of multiple statistics such as the mean, median, and the range. The boxplot on Statkey even allowed us to quickly determine outliers.
That way, you were able to reach conclusions regarding whether the variability of the average acceleration while sliding between a big vs small push was just normal noise or whether there are significant and persistent differences.
We will continue using the Boxplot tool in this course.
In your lab this week you will study how kinetic friction depends on the mass of the object.
Type | Variable |
---|---|
Independent | mass of IOLab+ stuff added to i |
Dependent | kinetic friction |
Control | surface |
There are often multiple control variables. It really refers to anything that you are NOT changing.
We will often look at cases where we have two quantitative (numerical) variables. We will use the scatterplot to visualize.
When we look at a scatterplot, we are interested to see whether there is a pattern, a trend.
For example, the image below shows a scatterplot created in Statkey of the ph vs average mercury for 53 different lakes in Florida.
While the data is pretty scattered, a careful observation of this scatterplot reveals there there seems to be a trend in that high ph lakes seems to be low average mercury while the reverse is true of low ph.
On the right of the image, you see the mean and standard deviation of each variable as well as the sample size. There is also a new statistics we have not seen before called the correlation.
The correlation often denoted r is a number between -1 and 1. Values close to +1 or -1 means strong association (positive or negative) while values close to 0 means no association.