Stat Exam 1

Null hypothesis

non directional
what you are actually testing
no assumption of a relationship
there will be no change

alternative hypothesis

something will happen
can be 1 tailed (directional) or 2 two tailed (non-directional)
informs about level of significance

one tailed hypothesis

directional
"i think you will say right more often"
p vs alpha
p

two tailed hypothesis

will just be "different"
nondirectional
p vs alpha/s
p < 0.025

Variables

the bigger thing that you are testing
"how intelligent are you”

observational unit

what you are literally collecting
"how intelligent are you”

act scores

biased sampling

When characteristics of the resulting samples are systematically different from characteristics of the population

unbiased sampling

When the distribution of the sample statistics, under repeated sampling from the same population, is centered at the value of the population parameter,

sample

the specific subset of participants you are collecting data from

population

the entire collection of observational units that we are interested in 

stastistics

measurements/calculations about our sample

parameters

measurements/calculations about our population

a simple random sample

gives every observational unit in the population the same chance of being selected. In fact, it gives every sample of size *n* the same chance of being selected.

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So, with a ___e of size 10, any set of 10 words is equally likely to end up as our sample.

convenience sampling

selecting the most readily available observational units 

cluster sampling

getting data from a population after divided it into different clusters

sample space

\: a list of all possible outcomes of a random process.

“the colors of marbles”

sample size

the total amount of your observational units

“you have 25 marbles, your _______ is 25”

Type 1 error

A in a test of significance occurs when the null hypothesis is true (there was no difference) but we decide to reject the null hypothesis. (you decide that there was a difference) This type of error is sometimes referred to as a false positive or a “false alarm.”

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Type 2 error

occurs when we fail to reject the null hypothesis (you say that there is no difference) even though the null hypothesis is false (but there was a difference). This type of error is sometimes referred to as a false negative or a “missed opportunity.”

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complement rule

The probability of an event happening is one minus the probability of the event not happening. 

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Addition rule for disjoint events: 

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* The probability of at least one of several events is the sum of the probabilities of those events *as long as* there are no outcomes in common across the events (i.e., the events are *mutually exclusive* or *disjoint*). 

Long run probability

over a large # of trials, the data will slowly turn into .5000

Central Limit Theorem

as sample size increases, the distribution of scores will become more normal “the bell curve” & narrower

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npi greater than/equal to 10

n(1-pi) greater than 10

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both need to be true for you to know that you have a big enough sample

normal distribution

a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean.

Empirical Rule

In any distribution, if you standardize it, you can predict the proportion of the scores

critical/z-values for confidence intervals

90%

95%

99S%

99\.9%

1\.645

1\.960

2\.576

3\.291

standard deviation

a measure of how dispersed the data is in relation to the mean. Low standard deviation means data are clustered around the mean, and high standard deviation indicates data are more spread out.

standard error

from repeated testing a population and using the deviation from each “mean”

rejection region

the set of values of the test statistic where the null hypothesis is reject. it us beyond the critical value

what is the p value needed to reject the null hypothesis in a two-tailed hypothesis

.025

what is the p value needed to reject the null hypothesis in a one-tailed hypothesis

0\.05

what is power

your ability to detect a significant result