always frame through the null hypothesis

type 1 error

• reject the null hypothesis when it is TRUE
  • say that something happened when it didn't
    • false positive

type II error

• fail to reject the null when it is FALSE
• say that nothing happened when it did
  • false negative

efforts to reduce one error, will result in the likelihood of the other

normal curve - symmetrical distribution, perfect bell-shaped curve, 50% below and above avg

normal distribution is mathematically defined

• shape is specified by relating each score along x axis with each frequency (along y-axis)

normal distribution is a theoretical

• behavioral data typically approximates a normal distribution
• as the sample size goes up, the dist. more and more closely resembles a normal dis.

mean, median and mode are all located @ the 50% percentile

• half of the data in a normal dis. fall above the mean, median, and mode. half fall below

normal dis. is symmetrical

• dis of data above the mean is te same as below the mean

standardization from indiv scores from normal dis. to shared scores from normal dis.

zscore converts any normal dis to a standard normal disc z score is the number of standard dev a particular score is from the mean

sign of z score (+ or -)

• positive X value is above the mean
• negative X value is below the mean
• value of the z-score equal the umber of sd between x and the mean of dis.

calculating the z score step 1 subtract the mean from the raw score step 2 divide by the sd

z=x-m/SD

population eq z=x-lu/o'

transforming z scores to raw scores step 1 _multiply the z score by the sd of the population step 2 _add the mean of the population to this product

x= z(SD) + m

empirical rule 99.7% of the data falls between 3 SD of the M 95% of the data falls between 2 SD of the M 68% of the data falls within 1 SD of the M

scores closer to the mean are more probable, scores farther are less probable

sample means are normally distributed True even when the population from which those means were

A distribution of means is less variable than a distribution of individual scores

Additional characteristics: The mean of the distribution tends to be the mean of the population

The standard deviation of the distribution tends to be less than the standard deviation of the population

σM = sd population of scores by the sq rt of total num of ppl within the population, within the sample