1. Data Organization I

2. Data Organization
Mean, mode, median. The mean in the “average” for a sample. Mean = (x1 + x2 + x3 … xn) / n

3. Example of Mean
There are really 500 fish in lake. You want to know what the average fish length is. It would take a long time to catch them all! You only catch 72 of them.

4. Example continued…
The average (all 500 fish) fish length is ACTUALLY 25inches. X1 + X2 + X3 … + X500 / 500 = 25 You could only catch 72 though, so the mean fish length is calculated to be 27.5 X1 + X2 + X3 … + X72 / 72 = 27.5

5. Example Continued…
The larger the sample size, the closer your mean is to the real value. 272 fish sample might have a mean of 26.3 for example is CLOSER to 25 because you caught more fish.

7. Data Organization
Standard Deviation is a measure of the spread of your values. Answers the question: “On average how far are your values from the mean?” Standard = what is typical Deviation = to move away from

9. Why use means?
The mean is an important tool for making inferences. An inference is when you make a well-informed guess based on empirical evidence. It involves making a prediction about the population based on characteristics of the sample.

10. Example 2
Determine whether or not high school students born in Florida have IQs above 110 on average. It would take a LONG time and lots of money to give an IQ test to millions of children in the state. So….

11. Example 2
We recruit 6000 volunteers between ages 15 and 19 and given them the IQ test. Our mean IQ score might be 105, but the actual average (the one we didn’t get because we didn’t recruit 10 million students), might be 108. We infer from our study sample that the average kid in FL has an IQ score of about 105 even though we didn’t test all students.

12. Example 2
Statistic: information computed from a sample set of some larger population. Type I error: Saying that there is an association (between independent and dependent variables) when there really isn’t an association. Type II error: saying that there is no association when there really is an association.