A way to organize data so that it has meaning!

 Descriptive - Allow us to make observations about the sample. Cannot make conclusions.  Inferential – Allow us to generalize our findings from the sample to the population. Allow us to make conclusions.

 Nominal – used to name or categorize  Ordinal – used to rank  Interval – consistent units of measurement, equal spacing, no true zero point  Ratio – same as interval, but with true zero point.

Measures of Central Tendency Measures of Dispersion

 Describe “typical” score in a distribution  Mode, median, mean

 Most frequently occurring number in data set  *only measurement of central tendency that can be used with nominal level data  If there are 2, call this a bimodal distribution

 Rank data ascending/descending order and find the “middle” number.  Best indicator of central tendency when there is a skew b/c it is unaffected by extreme scores  If n is odd, will be whole #  If n is even, will be between two values

 Arithmetic average of a set  Requires interval or ratio data  Sum of all scores/n  x 1 + x 2 + x 3 +…x n / n  n = sample size  Problem: Always pulled towards extreme scores or any skew of a distribution  Look at standard deviation to help understand how far away most scores are from the average.

 If they are all similar, you have very little distortion/skew!  If both the median and mode are to one side of the mean, your data is skewed or distorted!

 Bar graph  Height of bars indicate % or frequency  Titles and axis must be labeled to reflect the aim of the study!

 Amount of spread/variability in data distribution  How close is each individual score to the overall mean?

 Distance between top and bottom values of a set.  Not for nominal numbers!  Advantages: easy to calculate  Disadvantages:  distorted by extreme scores  misleading  Doesn’t tell us if the values are closely grouped around the mean or equally spaced across entire range

 The average of how far the scores are from the mean  Requires interval or ratio level data

1. Find mean of data set 2. Subtract mean from each value = deviation (d) 3. Square each (d) 4. Find the sum of d 2 5. Divide step 4 by (N-1) 6. Take the square root of this number!

 This tells you how far away (on average) the scores are from the mean in the sample.  The larger the standard deviation, the more variability in your data.  The less you can trust your mean score to be an accurate representation of a typical score!

 Clear lists  Stat button  4: clear list  2 nd function, 1 (for L1)  Enter…screen will say “done”  Enter your data  Stat button  Cursor will be on 1: edit, hit enter  Enter your data into L1, hit enter after each number  Magical calculation!  Stat button  Arrow over to Calc  Cursor will be on 1: 1-Var Stats, hit enter  Screen will say “1-Var Stats”  2 nd 1 (to tell it which list you want it to calculate from ) and hit enter  MAGIC!!!  S x = standard deviation

 On  Clear your list  Stat button  1: 4 clear list  2 nd 1 (L1)  Enter (done)  Enter your data  Stat button  1: Edit  Enter data into L1  Magically calculate all of your descriptive stats!!!  Stat button, arrow to calc  1: 1-Var Stats  2 nd 1 (L1)  Enter  Will list LOTS of numbers (mean, median, mode & S x =standard deviation)