• Descriptive Statistics: Summarizes and describes characteristics of a set of scores for a group
• Frequency Distribution: # of people who received each score
• Histogram: frequency distribution turned into graph.
• Measure of central tendency: mean (average), median (middle), mode (most often)
• Measure of variability: provide info about spread of scores (i.e range) (highest- lowest) least informative

Creating Deviate Score

Can We Help with Your Assignment?

Let us do your homework! Professional writers in all subject areas are available and will meet your assignment deadline. Free proofreading and copy-editing included.

Variance: distance between each store.

• Find mean of all scores (M)
• Find how much each values deviates from that (X)
• Square all those values, making them all positive and ascertain a value (x^2) [Variance]
• Divide Variance by mean, then square root it to get standard deviation

Normal Curve: symmetrical bell-shape curve that represent distribution in theory of the population. SD can divide curve into segments (68% of population fall within SD: -1-+1) (95% between SD: -2-+2 2% are above it) and nearly 100% between Sd: -3-+3 0.1% of being higher)

Based on the % of each segment allows us to predict the probability of an event transpiring. (through addition)

Psychologists TRY TO PREDICT WHY THESE EVENTS TRANSPIRE AND EXPLAIN THEM

Total variance= (variance account for by changing variable + variance not accounted for (random: error variance))

Applies to experiments and correlational studies

Pearson product-moment correlation coefficient: reflects direction/ strength of relation between two variables.

.5 coefficient DOES NOT mean that variable A and B and 50 percent related. SQAURE the r value  to get the amount of variance that the two variable share or have in commen. (.5^2=.25) 25% of A can account 25% of the data variance in B. REMEMBER CORRELATION IS NOT CAUSATION If predicator variable is closest related to criterion variable then accurate prediction can be made

Factor Analysis: Take a large amount of correlations and makes small cluster, each containing highly-related correlations (uses computers

Can identify correlations between variables in a correlation matrix but can’t explain what those relations mean. Psychologists must do the rest.

Things that influence statistics:

1) Size of the difference (Big difference= more significant)

2) Variability of measure (More variability= less significant)

3) # of measurements (More measurement= more significance)

Inferential statistics: how confident are we in drawing conclusions/ inferences about population based on sample

Finding must be Statistically significance, meaning it didn’t happen by chance and was based on facts. Performing test multiple times will allow for a more accurate distribution

Null Hypothesis: any difference between sample data is due to chance   