What is the effect of increasing the difference between sample means?
For the independent-measures t-statistic, what is the effect of increasing the difference between sample means? Increase the likelihood of rejecting the null hypothesis and increase measures of effect size.
What is indicated by a large variance for a sample of difference scores quizlet?
Terms in this set (85) A repeated measures study with a sample of n=16 participants produces a repeated measures t=2.00. If effect size is measuring using r(sqd), then r(sqd)=4/20. High variance for a sample of difference scores indicates that the treatment does not have a consistent effect.
Which of the following is the correct way to report the results of a hypothesis test and a measure of effect size using AT statistic?
Which of the following is the correct way to report the results of a hypothesis test and a measure of effect size using a t statistic? t(19) = 2.30, p < . 05, r2 = 0.42, A researcher selects a sample from a population with a mean of = 40 and administers a treatment to the individuals in the sample.
Which is a serious concern with a repeated-measures study quizlet?
In a repeated-measures experiment, each individual participates in one treatment condition and then moves on to a second treatment condition. One of the major concerns in this type of study is that participation in the first treatment may influence the participant's score in the second treatment.
Does increasing the sample variance increase the t statistic?
When the variance increases, so does the standard error. Since the standard error occurs in the denominator of the t statistic, when the standard error increases, the value of the t decreases.
What is the effect of increasing sample size?
Because we have more data and therefore more information, our estimate is more precise. As our sample size increases, the confidence in our estimate increases, our uncertainty decreases and we have greater precision.
How does sample variance influence the estimated standard error and measures of effect size?
The correct answer is B) Larger variance increases the standard error but decreases measures of effect size.
What does it mean when we see a large variance for a sample of difference scores?
What is indicated by a large variance for a sample of difference scores (i.e., a large variance of D scores)? A. A consistent treatment effect and a high likelihood of a significant difference.
Which of the following correctly describes the effect that decreasing sample size and decreasing standard deviation have on the power of a hypothesis test?
Which of the following correctly describes the effect that decreasing sample size and decreasing the standard deviation have on the power of a hypothesis test? A decrease in sample size will decrease the power, but a decrease in standard deviation will increase the power.
What is the advantage of a repeated-measures research study quizlet?
What is the advantage of a repeated-subject research study? It uses exactly the same individuals in all treatment conditions. A smaller number of subjects is required. Why might a repeated-measures study require half the number of subjects compared to a similar matched-subjects study with the same number of scores?
What is a serious concern with a repeated-measures study?
Which of the following possibilities is a serious concern with a repeated-measures study? You will obtain negative values for the difference scores. The results will be influenced by order effects. The mean difference is due to individual differences rather than treatment differences.
What happens when you increase variance?
When the variance increases, so does the standard error. Since the standard error occurs in the denominator of the t statistic, when the standard error increases, the value of the t decreases.
What happens to the variance of the sample mean when the sample size increases?
decreases Solution. Our result indicates that as the sample size increases, the variance of the sample mean decreases.
Why does variance decrease when sample size increases?
In other words, as the sample size increases, the variability of sampling distribution decreases. Also, as the sample size increases the shape of the sampling distribution becomes more similar to a normal distribution regardless of the shape of the population.
What happens to variance when sample size decreases?
That is, the variance of the sampling distribution of the mean is the population variance divided by N, the sample size (the number of scores used to compute a mean). Thus, the larger the sample size, the smaller the variance of the sampling distribution of the mean.
What happens when the sample variances increase when you conduct an independent sample t?
Increasing the variance of each sample will increase the size of the estimated standard error of the difference of the means. This decreases the size of the observer t which makes it harder to reject H0.
What is the effect of a large value for the estimated standard error?
It would increase the likelihood of rejecting the null and increase the risk of a Type I Error.
Why does variance decrease as sample size increases?
As the sample sizes increase, the variability of each sampling distribution decreases so that they become increasingly more leptokurtic. The range of the sampling distribution is smaller than the range of the original population.
How does variance change with sample size?
As a sample size increases, sample variance (variation between observations) increases but the variance of the sample mean (standard error) decreases and hence precision increases.
What effect does increasing the sample size have on the power of the test assuming all else remains unchanged?
Increasing sample size makes the hypothesis test more sensitive – more likely to reject the null hypothesis when it is, in fact, false. Thus, it increases the power of the test.
Why does increasing sample size increase power?
As the sample size increases, so does the power of the significance test. This is because a larger sample size constricts the distribution of the test statistic. This means that the standard error of the distribution is reduced and the acceptance region is reduced which in turn increases the level of power.
What is the advantage of repeated-measures research study?
The main advantage of a repeated-measures study is that it uses exactly the same individuals in all treatment conditions. That, there is no risk that the participants in one condition are substantially different from the participants from another.
Which of the following is an accurate distinction between repeated-measures and independent-measures research designs?
Which of the following is an accurate distinction between repeated-measures and independent-measures research designs? In repeated-measures research designs, each participant is measured once on the same variable.
Why do we worry about order effects in within subjects repeated measures designs?
Why can't we eliminate or hold order effects constant in a within-subjects experiment? We can't eliminate order effects because there is an order as soon as we present two or more treatments. Holding order constant—always assigning subjects to the sequence ABC—would confound the experiment.
What are practice effects in repeated measures design?
Practice effects in repeated measures design In repeated measures design, each participant is measured for multiple conditions in an experiment. For example, a group of people might be given extra help to see if it improves their math ability, and then they might be given access to an online help program.
What increases if variation increases?
Variation increases your costs. Think about a worker loading and unloading a chucker. The greater the variation of the worker's time, the fewer parts will be produced at the end of the shift. The closer the worker's “cycle time” matches that of the machine, the greater the number of parts at the end of the shift.
Does an increase in sample variance increase t-statistic?
The larger the variance of a sample, the less likely the t statistic will be significant, and the smaller the effect size will be. Finally, with a large sample, the standard error is typically going to be smaller, which means the statistic is more likely to be significant.
What happens when variance increases?
When the variance increases, so does the standard error. Since the standard error occurs in the denominator of the t statistic, when the standard error increases, the value of the t decreases.
What does an increase in variance mean?
A high variance indicates that the data points are very spread out from the mean, and from one another. Variance is the average of the squared distances from each point to the mean. The process of finding the variance is very similar to finding the MAD, mean absolute deviation.
What happens to the value of T when the number of scores in the sample increases?
Increasing the number of scores in each sample. Increasing the number of scores in each sample decreases the size of the estimated standard error of the difference of the means. This increases the size of the observed t which makes it easier to reject H0.