1. Descriptive and Inferential
Basic Statistics
Descriptive and Inferential

2. Why do we need it? Demystify data Organize data in a systematic way
“Number crunching”
Make inference about an event
Find trends in massive amounts of data, find a pattern produced by independent variable.

3. General Types Descriptive:
Equations are used to organize the data in terms of three measures of central tendency.
Mean, Median, and the Mode
Can not make clear inferences from these numbers.

4. Inferential: Equations are used to predict a trend in the data.
Allows for research to say “Effect is not due to chance”.
Looks for “significance” to be found or what is the probability of the trend.

5. Measurement Scales
In order for an analysis of the data to occur it must be in a numeric form.
The type of number conversion chosen will determine what statistic can be used.
Four measurement scales exist:
Nominal : to name or categorize
Ordinal : numbers show serial position

6. Interval : equal spacing between numbers.
Ratio scale : like interval but true zero point.

7. Techniques to Organize
Frequency distribution
Graphs: pie chart, bar graph, frequency polygon, line graph
Normal Curve (also called Bell Curve)
Physiological data is “normally distributed” ie: height, weight, shoe size, hat size
Skewedness: Positive and Negative

8. Correlations Co – relation between two or more variables.
NEVER cause and effect
Range = +1.0 to – 1.0
= perfect
= strong
= moderate (most psy research)

9. Scatter Plots

10. Types of scatter plot patterns. Click here.

11. Scatter Plot statistics:
For scatter plots, the following statistics are calculated:
Mean X and Y: the average of all the data points in the series. Maximum X and Y: the maximum value in the series. Minimum X and Y the minimum value in the series. Sample Size the number of values in the series. X Range and Y Range the maximum value minus the minimum value. Standard Deviations for X and Y values Indicates how widely data is spread around the mean. Line of Best Fit - Slope The slope of the line which fits the data most closely (generally using the least squares method). Line of Best Fit - Y Intercept The point at which the line of best fit crosses the Y axis.