67 terms

__ is a type of hypothesis that states the predicted effect does exist

Alternative

__ is a t-test that establishes whether two means collected from two different samples differ significantly

Independent

__ is a type of sample variance that must be calculated during an independent t-test when the sample sizes are unequal

Pooled

__ this type of test, aka a directional hypothesis, is used to determine if there is a significant relationship between variables in one specific direction

One tailed

__ you do this to the null hypothesis when the sample mean is associated with low probability of occurrence when null hypothesis is true

Reject

__ test that tests the hypothesis that the variances in different groups are equal

Levene's

__ type of hypothesis that states the predicted effect does not exist

Null

__ a t-test that establishes whether two means collected from the same sample differ significantly

Paired sample

__ a t-test that establishes whether a sample mean differs significantly from a population value

One sample

__ this type of test aka a non directional hypothesis, is used to determine if there is a significant relationship between variables in any direction

Two tailed

__ type of error that occurs when we believe that there is no effect in the population, when in fact there is

Type 2

__ type of error that occurs when we believe that there is a genuine effect in our population, when in fact there isn't

Type 1

Formulas cheat sheet

(in powerpoint)

If my experimental hypothesis were "Eating cheese before bed affects the number of nightmares you have," what would the null hypothesis be?

The number of nightmares you have is not affected by eating cheese before bed

If my null hypothesis is "Dutch people do not differ from English people in height" what is my alternative hypothesis?

All of the statements are plausible alternative hypotheses:

Dutch people are taller than English people.

English people are taller than Dutch people.

Dutch people differ in height from English people.

Dutch people are taller than English people.

English people are taller than Dutch people.

Dutch people differ in height from English people.

What does a significant test statistic tell us?

That the test statistic is larger than we would expect if there were no effect in the population, there is an important effect, and the null hypothesis is false

A Type 1 error occurs when:

We conclude that there is an affect in the population when in fact there is not.

A type 2 error occurs when:

We conclude that there is not an effect in the population when in fact there is.

"Children can learn a second language faster before the age of 7" is this statement:

A one tailed hypothesis

What is the alternative hypothesis for the following question: Does eating salmon make your skin glow?

People who eat salmon will have a more glowing complexion compared to those who don't.

What is the null hypothesis for the following question: Is there a relationship between heart rate and the number of cups of coffee drunk within the last 4 hours?

There will be no relationship between heart rate and the number of cups of coffee drunk within the last 4 hours.

Of what is the p probability?

P is the probability of observing a test statistic atleast as big as the one we have if there were no effect in the population (i.e., the null hypothesis were true)

Under a null hypothesis, a sample value yields a p-value of .015. Which of the following statements is true?

This finding is statistically significant at the .05 level of significance

SPSS problems

In hw?

What is a significance level?

A pre-set level of probability at which it will be accepted that results are due to chance or not

A researcher measured a group of people's physiological reactions while watching horror films and compared them to when watching comedy films. The resulting data were normally distributed. What test should be used to analyse the data?

Paired samples (dependent or related) t-test

Which of the following is an accurate description of the standard error?

It is the standard deviation of the sampling distribution of a statistic

The degrees of freedom for the paired samples t-test are:

n-1

Three types of t tests

1. Independent sample

2. Paired sample

3. One sample

2. Paired sample

3. One sample

To go from two tailed to one tailed...

Divide by 2

How is a t statistic calculated?

Mean difference/standard error of mean

When analysing the cancer survival data, a p-value set at .05 or .01 is most likely to protect against which type of error?

Type 1 error

Consider an independent t test, which of the following expressions best represents the degrees of freedom?

n-2

If a significance result of .000 is presented, how should this be reported?

p < .001

Steps to hypothesis testing

1. State the hypotheses (alternative vs null)

2. Set the criteria for a decision (.05, .01, etc)

3. Compute the test statistic (conduct the analysis)

4. Make a decision (supported or not supported)

2. Set the criteria for a decision (.05, .01, etc)

3. Compute the test statistic (conduct the analysis)

4. Make a decision (supported or not supported)

Alternative hypothesis (H1)

That an effect will be present. What we usually think of when coming up with a prediction. Example: if you imagine eating chocolate you will eat less of it

Null hypothesis (H0)

Opposite of alternative hypothesis. Usually states that an effect is absent. Example: if you imagine eating chocolate you will eat the same amount as normal

__ is a mathematical formula that allows researchers to determine the likelihood of obtaining outcomes if the null hypothesis were true

Test statistic

Value of the test statistic is used to make a decision regarding the __

Null hypothesis

1. State the hypotheses

-Start by assuming null is true (just like defendant)

-But we think our hypothesis is true

-Conduct a study to find evidence that null isn't true

-Null and alternative hypotheses will involve a population (what were interested in generalizing to)

-But we think our hypothesis is true

-Conduct a study to find evidence that null isn't true

-Null and alternative hypotheses will involve a population (what were interested in generalizing to)

2. Set the criteria

-Significance level or level of significance

-Criterion of judgement upon which a decision is made

-Need enough evidence to show guilt beyond a reasonable doubt

-Behavioral research=5% usually

-Based on the probability of obtaining a statistic measured in a sample if the null hypothesis were true

-Criterion of judgement upon which a decision is made

-Need enough evidence to show guilt beyond a reasonable doubt

-Behavioral research=5% usually

-Based on the probability of obtaining a statistic measured in a sample if the null hypothesis were true

3. Compute the test statistic

Value of the test statistic is used to make a decision regarding the null hypothesis

4. Make a decision

2 decisions researchers can make:

1. Reject the null hypothesis: sample mean is associated with low probability of occurrence when null hypothesis is true

2. Fail to reject the null hypothesis: sample mean is associated with high probability of occurrence when null hypothesis is true

1. Reject the null hypothesis: sample mean is associated with low probability of occurrence when null hypothesis is true

2. Fail to reject the null hypothesis: sample mean is associated with high probability of occurrence when null hypothesis is true

How do I make that decision?

-Look at your p value. How likely it is to get a result like this if null is true. Compared to a level of significance (criteria)

-Is the p-value significant? Usually p<.05 in behavioral research then reject null

-Is the p-value significant? Usually p<.05 in behavioral research then reject null

__ is rejecting a null hypothesis that is actually true

Type 1 error. False positive. You're found guilty of a crime you didn't commit

__ is not rejecting a null hypothesis that is actually false

Type 2 error. False negative. You're found guilty but not found guilty in trial. Told you don't have a disease when you really do.

If a test statistic has a p value <.05 or 5%, what is the decision for this hypothesis test?

Reject null hypothesis

One tailed hypothesis

Directional, hypothesis specifies some direction

Two tailed hypothesis

Non directional, hypothesis doesn't specify any direction

T statistic

Type of statistic that is used in hypothesis testing. Inferential statistic used to determine # of SD's in a t-distribution that a sample mean deviates from the mean value or mean difference stated in null hypothesis

T statistic formula in words

t=(observed difference between sample means)-(expected difference between population means if null hypothesis is true)/(estimate of the standard error the difference between two sample means)

__ used to test hypotheses concerning single group mean selected from a population

One sample t test

__ used when there are 2 experimental conditions and different participants assigned to each condition

Independent t test

__ used when there are 2 experimental conditions and same participants took part in both conditions

Paired samples t test

What information do you need in order to find the critical t?

1. Two/one tailed

2. Degrees of freedom

3. Criterion (.05 or .01)

2. Degrees of freedom

3. Criterion (.05 or .01)

In one sample t test, if p<.05 it is ___

Significant

If obtained t is higher than the critical t then we __

Reject the null

3 steps for calculating unequal sample size

1. Pooled sample variance

2. Estimated sample error for the difference

3. T test formula

2. Estimated sample error for the difference

3. T test formula

3 steps for calculating equal sample size

1. Sample variance

2. Estimated sample error for the difference

3. T test formula

2. Estimated sample error for the difference

3. T test formula

Degrees of freedom for independent samples

(n-1)+(n-1)

__ assesses the assumption that the variances are equal

Levene's test

If null: p>.05 then...

Look at the 1st line (equal variances assumed), the variances are equal

If alternative: p<.05 then...

Look at the 2nd line (equal variances not assumed), the variances are not equal

2 methods for paired sample t test

1. Pre-post design

2. Within-subjects design

2. Within-subjects design

__ is the measure dependent variable before (pre) and after (post) a treatment

Pre-post design

__ is when they observe participants across many treatments but not necessarily before and after treatment

Within-subjects design

The __ the value, the less likely a sample mean difference could occur if null hypothesis were true

Larger