**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**

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

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That was a very helpful, easy to read and conceptualise post~ thanks.