exam 4 Flashcards by Lauren Kozik

inferential statistics

used to determine whether results match what would happen if we were to construct experiment again and again

(basically testing to see if sample means reflect a true difference in population means)

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formal term for null hypothesis

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goal of inferential statistics

determine chance (probability) of getting a particular outcome (sample) if our assumption about the world (null hypothesis) is true.

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what do you do if probability is too low

reject null hypothesis

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how to calculate probability

p=frequency/all outcomes-

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null hypothesis

population means are equal-observed differences is due to random error

“there is no change, difference or relationship in the general population”

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research hypothesis

population means are not equal

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binomial distribution

likelihood that a value will take one of two independent values under a given set of parameters or assumptions. The underlying assumptions of the binomial distribution are that there is only one outcome for each trial, that each trial has the same probability of success, and that each trial is mutually exclusive, or independent of each other.

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in a coinflip test, the binomial distribution represents the..

null hypothesis

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possible outcomes=

population

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probability of getting a particular outcome

sample

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if the probability of your “sample” is high

enough given your null hypothesis…(“population”),

“fail to reject the null hypothesis”

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if sample is too unlikely you..

“reject null hypothesis”

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why do we say fail to reject null

Hypotheses can be rejected with certainty,

but the correct single one may never be identified…

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research hypothesis in formal terms

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statistical significance

a significant result when the outcome has a very low probability of occurring if the population means were equal

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what is the probability required for significance called

alpha level

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alpha level

the probability required for significance

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research hypothesis is also called..

alternative hypothesis

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how to specify null hypothesis formally

u1=u2

u1=0.5

u1=u2=u3=u4..(iv has more than two levels)

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how to specify if researcy hypothesis is correct in formal terms

u1 =/= u2

u1=/=0.5

u1>0.5, etc

“at least one differs from the rest’

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sampling distributions are based on..

the assumption that the null hypothesis is true

deals with probability charts?

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p value

actual probability of result if null hypothesis was true

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importance of sampling size

as sample size increases, you’re more likely to obtain an accurate estimate of the true population value

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Type I error

rejecting the null hypothesis (H0) when it is actually true

Type II error

failing to reject (or, accepting) H0 when an alternative hypothesis (H1) is actually true.

t test

used to examine weather two groups are statistically different to each other. reflects all possible outcomes we could expect if we compare the means of two groups and null hypothesis true

what is the t value

ratio of two aspects of data, the difference between means and the variability within groups

group difference

the variance between obtained means. (deals with t test) under null hypothesis, you expect this to be 0

the value of t increases as...

the difference between your obtained sample means increases

degrees of freedom

(df) its the total number of participants minus the number of groups

what determines if something is a one or two tailed test?

depends on whether you origionally designed your study to test a directional hypothesis

what do you do if you cant calculate the probability of an outcome (step 2 of inferential statistics)

convert value to a statistic (eg a score/measure/etc) then determine the probability of getting that statistic (USE Z SCORE! Z SCORE IS THE STATISTIC)

statistic

a score/measure/etc

statistical vs practical significance

statistical significance- whether an effect exist practical-refers to the magnitude of the effect

another name for significance level

alpha level

probability of making a type one error is determined by..

determined by choice of significance or alpha level

probability of type 2 error is determined by..

alpha level sample size effect size

sampling distribution

other possible statistics of the same size

The Central Limit Theorem

“When variables are added (such as w/ means), the distribution of these new values (e.g., 𝑋തs) will approach a normal distribution even if the original variables themselves are not normally distributed”

the central limit theorem suggest that..

with a large enough sample size, almost every sampling distribution becomes normal

standard error

average distance between a sample mean and a population mean | standard deviation of the distribution of sample mean and pop mean

factorial design

an experiment with more than one independent variable (also called factor)

main effects

the effect one IV has by itself on the DV in a factorial design

how do you know when theres interaction

when effect of one IV on the DV depends on the particular level of the other IV

format for factorial designs

#levels in first IV X #levels in 2nd IV Xnumber of levels in 3rd IV

The ____ the t (or z) ratio, the more likely the results are ___

Larger Significant

R squared value

Represents the percentage of variability in u that can be accounted for in x

A criterion variable is generally analogy’s to an ___variable in the experimental method

Dependent

Precictor variable

Variable used in regression to predict another variable | Predicts outcome of critertion. Can also be thought of as dependent variable

John finds Pearson r coefficient between variable a and b to be 0. Before concluding there is no relationship, what should he do?

Construct a scatter plot of the data

T test and Pearson correlation are used to test what kind of data?

Interval

Multiple correlation is a technique for

Increasing accuracy of prediction

Ways to commit a Type two error

Alpha is too low Sample size is too small small effect/small effect size Experimenter error such as bad instructions or weak manipulation

Main effect

The effect that one independent variable has by itself on the dependent variable

The mean difference between levels of one factor are called the____

Main effects of that factor

What do you plot to determine if there’s a main effect between interactions

Cell means

What would a plotted data point look like if there’s a main effect

The data point won’t overlap Overall differences in bar heights

How would you determine if there’s no main effect

If plots overlap

Interaction

When effect of one independent variable on the dependent variable depend on the particular levels of the other independent variables

How to determine if there’s NO interaction between factors

Parallel lines or no changes in bar patterns

R squared

Amount of variance explained Percent of shared variance between two variables

What is y prime

The predicted value of y (critertion variable)

The critertion variable

Predictor variable

The y value at the point where the regression line crosses the y axis The value of when x=0

Slope of the line | Related to but not the same as r

Restricted range

Range of values that has been condensed or shortened It makes the correlation misleading

A correlation coefficient is always used to

Describe the strength and relationship between two variables

Multiple correlation uses many ______ variables and one ___\\\

Many predictor One criterion

Effect size

quantitative measure of the magnitude of the experimenter effect.