Comparing two means

Pregunta 1 de 25

1

T-test are useful for detecting what kind of effect? BEST RESPONSE

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An association between variables.
A difference between two means.

Explicación

Pregunta 2 de 25

1

Select ALL TRUE statements.

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Unlike methods such as regression and correlation methods such as ANOVA and t-tests allow us to make casual inferences.
Unlike methods such as ANOVA and t-tests methods such as regression and correlation allow us to make casual inferences.
Each of these tests mentioned can be considered mathematically identical.
The type of statistical test is not what is relevant to causal inference. It's the fact that in experiments we manipulate the causal variable systematically to see it's effect on an outcome.
In correlational research we observe the co-occurrence of variables, we do not manipulate the causal variable then measure the effect on the outcome. Therefore we cannot compare the effect of when the causal variable is present against when it is absent. Thus we cannot say which variable causes a change in the other, we can only say that both variables co-occur in a certain way.

Explicación

Pregunta 3 de 25

1

An independent design (alternatively between-groups or between-subjects design) is

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an experimental design in which different conditions utilise different (organisms etc). (e.g in psychology this would mean using different people). As such the resulting data can be considered independent.
an experimental design in which different conditions utilise the same organisms. (e.g in psychology this would mean using the same people to take part in two experimental conditions).
an experimental design in which all variables can be considered independent (i.e there are no dependent variables)

Explicación

Pregunta 4 de 25

1

A repeated measures design (alternatively, a within-subjects design) is

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an experimental design where different conditions utilise the same organisms (i.e. in psychology such a design would mean that the same people take part in all experimental conditions). As such the resulting data are related.
an experimental design in which different conditions utilise different organisms. (e.g in psychology this would mean that different conditions conducted on different people.
A design that only utilises independent variables.
A design that only utilises dependent variables.

Explicación

Pregunta 5 de 25

1

Median splits, i.e dividing people into groups based on the median is generally a good idea.

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VERDADERO
FALSO

Explicación

Pregunta 6 de 25

1

An experiment that compared the ratings of a film where one group of participants were subjected to small electric shocks compared to a control group who just watched the film could be considered a

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Repeated measures design
Within-subjects design
Independent design
Between-subjects design

Explicación

Pregunta 7 de 25

1

What are the problems with NHS tests for normality (Kolmogorov-Smirnov) and homogeneity of variance (Levene's)

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WRITE OUT ANSWER AND CHECK IN THE TEXT BOOK. pp184
REVISE AT THE END OF THE TEST.

Explicación

Pregunta 8 de 25

1

A t-test can be explained in terms of linear regression.

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VERDADERO
FALSO

Explicación

Pregunta 9 de 25

1

What is the difference between independent and paired samples t-test?

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Independent-samples t-test -- This test is used when there are two experimental conditions and different participants are assigned to each condition.
Paired-samples t-test -- This test is used when there are two experimental conditions and the same participants took part in each condition.
Independent-samples t-test -- This test is used when there are two experimental conditions and the same participants took part in each condition.
Paired-samples t-test -- This test is used when there are two experimental conditions and different participants are assigned to each condition.

Explicación

Pregunta 10 de 25

1

Which of the following are other names for a paired-samples t-test?

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Matched-pairs t-test
Dependent t-test
Independent measures t-test

Explicación

Pregunta 11 de 25

1

T-tests: If samples come from the same population, then we expect their means to be roughly equal. Although it is possible for their means to differ by chance, we would expect large differences in their means to occur very infrequently. Under null hypothesis significance testing we assume that the experimental manipulation has no effect on the participants; therefore we expect that the sample means to be very similar.

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VERDADERO
FALSO

Explicación

Pregunta 12 de 25

1

T-tests: We compare the difference between the sample means that we collected to the difference between sample means that we would expect to obtain if there was no effect. We use the standard error as a gauge of the variability between sample means.

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If the standard error is small, then we would expect most samples to have very similar means. However if the standard error is large, then it is possible that larger differences between means might occur due to chance.
If the standard error is small, then it is possible that larger differences between means might occur due to chance. However if the standard error is large, then we would expect most samples to have very similar means.

Explicación

Pregunta 13 de 25

1

If the difference between the samples we have collected is larger than we would have expect based on the standard error then we can assume one of two things:
There is no effect and we have, by chance, collected two samples that are atypical of the population from which they came. OR The two samples come from different populations but are typical of their respective parent population. In this scenario, the difference between samples represents a genuine difference between the samples (and so the null hypothesis is unlikely).

The larger the difference becomes between the two sample means:

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The more confident we become that the second hypothesis is correct. If the null hypothesis is incorrect, the we gain confidence that the two sample means differ because of the different experimental manipulation imposed on each sample. (causal inference)
The more confident we become that the first hypothesis is correct. If the null hypothesis is correct, and thus we have no evidence to infer that the two sample means differ because of the different experimental manipulation imposed on each sample.

Explicación

Pregunta 14 de 25

1

A t-statistic is equal to

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(The observed difference between the two means minus the expected difference between the two means (if there was no effect)) divided by the estimate of the standard error of the difference between two sample means.
(The observed difference between the two means minus the expected difference between the two means (if there was no effect)) divided by the estimate of the standard error of the combined sample mean
(The estimate of the standard error of the difference between two sample means) divided by the observed difference between the two means minus the expected difference between the two means (if there was no effect).
(the standard error of the combined sample mean) divided by the observed difference between the two means minus the expected difference between the two means (if there was no effect).

Explicación

Pregunta 15 de 25

1

When different participants participate in different conditions, pairs of scores will differ not only because of the experimental manipulation, but also because of other sources of variance (such as individual differences in age, IQ etc).

This statement applies to:

Selecciona una de las siguientes respuestas posibles:

Independent t-test
Paired-samples t-test

Explicación

Pregunta 16 de 25

1

To compute a t statistic you do not actually need any raw data, it can be computed with simply the means, standard deviations and sample sizes.

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VERDADERO
FALSO

Explicación

Pregunta 17 de 25

1

The standard error of the difference refers to

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The variability of groups in the population
The sampling error of the mean
The standard deviation of the sampling distribution of the mean
The standard deviation of a sampling distribution of differences between means.
A measure of variability of differences between sample means.
A measure of the unsystematic variation within the data.
A measure of the systematic variation within the data.

Explicación

Pregunta 18 de 25

1

We divide by the standard error because:

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It standardises the average difference between conditions so that we can compare t statistics without having to worry about the scale of measurement used to measure the outcome variable. That is two t-statistics are comparable even if they come from different experiments.
It contrasts the difference between means that we have, against the difference that we could expect to find given how well the samples represent to populations from which they came.
It contrasts the difference between means that we have, against the alpha level and thus provides a measure of the probability that the difference occured by chance.

Explicación

Pregunta 19 de 25

1

T-statistics are cool.

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A t statistic is simply a ratio of the systematic variation in the experiment to the unsystematic variation.
A t statistic is simply a ratio of the systematic variation in the experiment compared to the differences between observed and expected values.

Explicación

Pregunta 20 de 25

1

T statistics are still cool.

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If the experiment creates any kind of effect, then we would expect the systematic variation to be greater than the unsystematic variation and thus (at the very least) the T statistic should be greater than 1.
If the experiment creates any kind of effect, then we would expect the unsystematic variation to be greater than the systematic variation and thus (at the very least) the T statistic should be greater than 1.
T statistics are always less than 1.

Explicación

Pregunta 21 de 25

1

Which of the following is accurate?

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SSm (Model Sum of Squares): The proportion of the variance explained by the model.
SSm (Model Sum of Squares): equals SSt-SSr
SSm (Model Sum of Squares): The difference between the mean line and the regression line for all values of x.
SSr (Residual Sum of Squares): The proportion of variance not accounted for by the model.
SSr (Residual Sum of Squares): The sum of all deviations from the regression line squared.
SSr (Residual Sum of Squares): The sum of all deviations from the mean squared.
SSt (Total Sum of Squares): The sum of all deviations from the mean squared.
SSt (Total Sum of Squares): The total variance in the data
SSt (Total Sum of Squares): SSr+SSm
SSm (Model Sum of Squares): The sum of all deviations from the regression line squared.

Explicación

Pregunta 22 de 25

1

What is OLS?

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Ordinary Least Squares Regression
Ordinary Leveraged Sum of Squares Regression
Ordinal Least Squares Regression

Explicación

Pregunta 23 de 25

1

OLS predicts the regression line by

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calculating the line that will have the lowest residual sum of squares.
calculating the line that will have the lowest model sum of squares.
calculating the line that will have the lowest total sum of squares.

Explicación

Pregunta 24 de 25

1

In order these values denote:

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An observed value of y, the mean of y, a predicted or estimated value of y
The mean value of y, a predicted or estimated value of y, an observed value of y
The mean value of y, an observed value of y, a predicted or estimated value of y
A predicted or estimated value of y, the mean value of y, and observed value of y

Explicación

Pregunta 25 de 25

1

In regression R squared is a measure of

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How well the regression line fits the observed data.
The proportion of variance explained by the regression line
The correlation between the x values and y values
The proportion of variance explained by the co-variance as scaled by the standard deviation

	Creado por Dan Myles hace más de 9 años

Test sobre Comparing two means, creado por Dan Myles el 08/03/2015.

Comparing two means

Pregunta 1 de 25

T-test are useful for detecting what kind of effect? BEST RESPONSE

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 2 de 25

Select ALL TRUE statements.

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 3 de 25

An independent design (alternatively between-groups or between-subjects design) is

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 4 de 25

A repeated measures design (alternatively, a within-subjects design) is

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 5 de 25

Median splits, i.e dividing people into groups based on the median is generally a good idea.

Selecciona uno de los siguientes:

Explicación

Pregunta 6 de 25

An experiment that compared the ratings of a film where one group of participants were subjected to small electric shocks compared to a control group who just watched the film could be considered a

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 7 de 25

What are the problems with NHS tests for normality (Kolmogorov-Smirnov) and homogeneity of variance (Levene's)

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 8 de 25

A t-test can be explained in terms of linear regression.

Selecciona uno de los siguientes:

Explicación

Pregunta 9 de 25

What is the difference between independent and paired samples t-test?

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 10 de 25

Which of the following are other names for a paired-samples t-test?

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 11 de 25

Selecciona uno de los siguientes:

Explicación

Pregunta 12 de 25

T-tests: We compare the difference between the sample means that we collected to the difference between sample means that we would expect to obtain if there was no effect. We use the standard error as a gauge of the variability between sample means.

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 13 de 25

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 14 de 25

A t-statistic is equal to

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 15 de 25

When different participants participate in different conditions, pairs of scores will differ not only because of the experimental manipulation, but also because of other sources of variance (such as individual differences in age, IQ etc). This statement applies to:

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 16 de 25

To compute a t statistic you do not actually need any raw data, it can be computed with simply the means, standard deviations and sample sizes.

Selecciona uno de los siguientes:

Explicación

Pregunta 17 de 25

The standard error of the difference refers to

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 18 de 25

We divide by the standard error because:

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 19 de 25

T-statistics are cool.

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 20 de 25

T statistics are still cool.

Selecciona una de las siguientes respuestas posibles:

Explicación

When different participants participate in different conditions, pairs of scores will differ not only because of the experimental manipulation, but also because of other sources of variance (such as individual differences in age, IQ etc).

This statement applies to: