1. TYPES OF DATA KEEP THE ACTIVITIES ROLLING Data, Standard Deviation, Statistical Significance

2. Nominal Data: data that simply identifies categories. EX—Yes/No, year in school (senior, junior, etc.) Ordinal Data: identifies the order in which data falls in a set. Any ranking of items, from one-hit wonders to class rank to home-run sluggers, is done as ordinal data. Interval Data: includes data that falls within a number line that has a zero point. EX—height and weight. Ratio Data: includes data that falls in a number line where zero is just another number on the line. EX— temperature.

3. HISTOGRAMS A Histogram is a graphical display of data using bars of different heights. It is similar to a Bar Chart, but a histogram groups data into ranges.

4. LET’S MAKE A HUMAN HISTOGRAM

5. MORE TERMINOLOGY Define the following terms in your own words and give an example… -Mode -Histogram -Median-Normal Curve -Mean-Skew -Range-Outlier -Standard Deviation-Discrete Variable -Dichotomy-Trichotomy -Continuous Variable-Discrete Variable

6. STANDARD DEVIATION Standard deviation is computed as the average distance from the mean The larger the standard deviation, the more spread out the scores are Just like the mean, the standard deviation is sensitive to extreme scores. To get the standard deviation – Find the difference between the mean and each score – Square each difference – Get the sum of all the differences – Divide the sum by the size of the sample (-1) – Take the square root of the result Variance is simply the standard deviation squared (variance is a statistic that is used in some calculations)

7. Significance Testing Significant Result in Research – Not necessarily large or important – Not necessarily dramatic – But probably did not occur by chance Null Hypothesis – Opposite of Hypothesis Hypothesis: Anxiety reduces test performance Null Hypothesis: Anxiety does not effect test performance.

8. p-level (Probability Level) Probability that your null hypothesis is correct Probability that the statistic is really zero Examples: -Difference in means -Correlation coefficient p <.05 (p <.01) Your null hypothesis has less than a 5% chance of being right You have less than a 5% chance of being wrong

9. TYPE I and TYPE II ERRORS Type I Error (false positive) – Deciding that one variable has an effect on (or relationship to another variable) when it doesn’t – Incorrectly rejecting the null hypothesis and accepting the hypothesis – p-level gives us the odds of making this kind of error

10. Type II Error (false negative) – Deciding that one variable does not have an effect on (or a relationship to) another variable when it does – Incorrectly accepting the null hypothesis and rejecting the hypothesis – There is no easy way to estimate the odds of this kind of error