Psych 5 Final

Known population before treatment and unknown population after treatment

research

effectiveness

means

• compare sample mean to population mean
• or compare two or more sample means to eachother

• chance
• sampling variability
• sampling error

chance

Statistical method using sample data to evaluate a hypothesis about a population

• -M is unlikely to have occurred by chance
• -M is in the extreme tails of the sampling distribution

• -If M would occur 5% or less of the time
• -p<0.05

no difference between the means, called the null hypothesis

• -u
• -if M is not close to u, we reject the null hypothesis

inferential statistics

• -State the hypothesis
• -Set the criterion for a decision
• -Compute the test statistic
• -Make a decision

-Ho: Predicts there is no change, difference, or relationship between our treatment and the general population

-H1: There is a change in the population after we apply the treatment

mutually exclusive (if one is true, the other cannot be)

• -Also known as a two-tailed hypothesis
• -Direction of difference is not specified

Create a decision rule, find the critical value, put probability, one or two-tailed

boundary between likely/unlikely outcomes

area under the curve more extreme than the critical value

Reject H0 if observed test statistic falls in the critical region (exceeds critical value)

See paper

Reject or fail to reject H0

• -chance
• -statistically significant
• -prove or disprove

• -chance
• -statistically significant 
• -H1

-prove or disprove

• -Rejecting a null hypothesis when it's actually true
• -Concluding a treatment has an effect when in reality it doesn't

• -We do this with our alpha level, or p value
• -a=0.05 --> there's a 5% chance we will falsely reject the null hypothesis
• 5% chance of type 1 error
• -a=0.01--> there's a 1% chance we will falsely reject the null hypothesis

• -Failing to reject a null hypothesis that is false
• -The test failed to detect a real treatment effect

-probability of making a type II error when H_o is false

• -power
• -effect size and sample size

• -Effect size is bigger because big effects are easy to use
• -Sample size is larger because large samples make it easy to detect tiny effects

• -Increase sample size (n) is the best possible way
• -Increase treatment effect size
• -Choose less stringent alpha level
• -Use a one-tailed test

• -The absolute truth
• -p (making a mistake)
• -Type I and Type II error
• -0.05, 0.01 or 0.001

Descriptive statistic that indicates magnitude of an effect

Cohen's d, r2, eta-squared

see paper

-difference between means in standard deviation units

• -0.2
• -0.5
• -0.8

• -each tail
• -in the lower negative tail only-for one tailed tests, critical value is smaller

• Distribution of sample means is normal
• Individuals were randomly sampled
• Independent observations
• Know that SD does not change with treatment

• -Two means
• -Z score
• -probability
• -tails

Distributions with one clear peak

Distributions with two clear peaks

• A probability distribution for which all of the values that a random variable can take on or occur with equal probability
• Or the shape of a graph that has no peaks and valleys

Can be divided at the center so that each half is a mirror image of the other

• Occurs when a normal random variable has a mean of zero and a standard deviation of one
• The normal random variable is called a z score

Distributions with fewer observations on the right

Distributions with fewer observations on the left

• All normal distributions look like a symmetric, bell-shaped curve 
• Associates the normal random variable X with a cumulative property

The tail on the right side is longer or fatter than the left side

Tail on the left side of the distribution is longer or fatter than the left side

Portion of the distribution having a large number of occurrences far from the "head" or central part of the distribution

Middle of the bell curve

• -Probability of making a correct decision when H_o is false
• -Ability to detect a real treatment effect