Hypothesis Testing

Question 1

Null Hypothesis

Answer 1

A hypothesis that is tested in an experiment

Question 2

Null Hypothesis notation

Answer 2

Ho: X=B

Question 3

Signifigance level

Answer 3

Can be notated as α. 1.96 is a “critical value”

Question 4

Critical Value

Question 5

Test Statistic

Answer 5

The number that can be greater or less than a critical value, and thus be significant or not significant

Question 6

Rejection Region

Answer 6

The value below/above the critical value. If test statistic is in this region, we can reject the Null Hypothesis. If It’s not, we fail to reject the null hypothesis. (That term is specific, we cannot “accept” a null hypothesis, only fail to reject it.”)

Question 7

Types of logical “errors”

Answer 7

Type I: If there is not a difference, a significance, and you find that there was, you made a type one error
Type 2: If there is a difference, and you find there isn’t, you made a type two error.

Question 8

α

Answer 8

Whatever alpha is, I’m willing to accept

Question 9

beta

Question 10

α level

Answer 10

The largest probability of committing type 1 error we “allow”, while rejecting/not accepting the No (null hypothesis)

Question 11

Alternative Hypothesis (H1)

Answer 11

The idea that researchers are trying to prove as an “alternative” to the null: this is something akin to what we believe to actually be true, versus the null hyp.

Question 12

beta (β) error

Answer 12

When a null, in reality, is incorrect/untrue, yet we don’t find evidence to disprove it or prove the alt hypothesis, we have made a β error.

Question 13

Cohen’s d

Answer 13

One measure of effect size. It tells us precisely how many SD above/below the null hypothesis of our population

Question 14

Cohen’s effect size conventions

Answer 14

An effect size measured using Cohen’s d needs context:

d

Question 15

directional tests (one-tailed test)

Answer 15

A hypothesis test where the alt hypoth is in the form >/< the value of the null. Indicates a “specific alternative” to the null

Question 16

effect size

Answer 16

A stat measure of the size of the “change” that a variable has had in an experiment. Measured by the differences of scores in the population between treatment and control, given that the samples used were randomly sampled.

Question 17

hypothesis testing

Answer 17

A structured process by which scientists draw likely conclusions about a population from observations.

Question 18

critical value

Answer 18

If you obtain sample means beyond this value on a distribution, you would reject the null hypothesis

Question 19

null hypothesis (Ho)

Answer 19

The assumption about what is “actually” present or occurring in a population: the goal of hypothesis testing is to make a decision to reject or accept this idea. Needs to be reasonable given the data available.

Question 20

obtained value

Answer 20

the value of the test statistic: if the sample z-statistic (or whatever statistic you’re measuring) falls outside of the α, you reject the null (it was unlikely to occur by chance)

Question 21

power

Answer 21

the probability of rejecting a null when it is false. Put differently, it is the probability of a randomly selected sample will show the falsehood of a null when it’s actually false.

Question 22

test statistic

Answer 22

A formula that gives us how many SD a sample is from the value of our stated Null.

Question 23

one sample z test

Answer 23

The statistical hypothesis test used when we know a mean and SD in a single population.

Question 24

p-value

Answer 24

the probability of obtaining a sample value given that the null is true. In behavioral science, typically, if it’s less than 5 percent, we have a decent amount of certainty that the null is untrue

Question 25

effect

Answer 25

In experimental studies, an effect is when you are testing an experimental group, and the means you obtain have small likelihood of happening given that a basic assumption is true (null). You have a measurable and unlikely impact on your subjects.

Question 26

nondirectional tests

Answer 26

Hypothesis tests where your goal is simply to see whether a mean in a sample is likely different (≠) or the same (=) from the mean stated in the null. (α cutoff at 5%/1.96SD is split in half, .0250 in each tail)

Question 27

one-tailed test

Answer 27

(directional tests), tests where the alt hypothesis needs to be the null. You place the critical value in the corresponding section of the distro: if you’re looking for

Question 28

Type I error

Answer 28

A “false positive”: when you incorrectly reject the null hypothesis. It is true, but for some reason you found likelihood that it was not. (Like finding an innocent person guilty)

Question 29

type III error

Answer 29

When you place the rejection region in the wrong tail. (Only occurs in one tailed tests where the rejection region is placed in one or the other)

Question 30

z-statistic

Answer 30

A z-transformation, creates a value along the standard normal distro (M=0, SD=1)

Question 31

Type II error

Answer 31

The incorrect decision to “keep” a null that actually isn’t true. Also called a “false negative” or beta (β) error.

Question 32

One sample z-test

Answer 32

A statistical formula used when you know the mean and variance/SD, and a single population

Question 33

test statistic

Answer 33

a formula that allows us to know how many SDs a sample outcome is from the null (gives us a p-value)

Question 34

test statistic

Question 35

statistical significance

Answer 35

The result of a sample mean being less than 5% likely given a true null. The result shows us that something is happening in the population we’re testing.

Question 36

significance testing

Answer 36

The process of trying to find significance in a population by experimentation.

Question 37

rejection region

Answer 37

The area beyond a critical value in a one-tailed or two-tailed test: if a sample mean or some other sample stat we measure falls here, the null hypothesis is rejected.

Question 38

significance level

Answer 38

α, or the percent/distance that a sample statistic/z value must be before we are “willing to accept” that the null is false. Usually a small value: you want the likelihood that something is the case given a true null to be very small.

Question 39

two-tailed tests

Answer 39

Tests where the alternative hypothesis (H1) is specifically above OR below the null mean/parameter. You’re declaring specifically what direction you believe the experiment or population should shift relative to the basic assumption that is on trial.

Question 40

Cohen’s D calculation

Answer 40

Cohen’s D= M-μ/σ