What is the effect of increasing sample variance?
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 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.
Which of the following is an assumption of the independent measures t-test?
T-Test Assumptions The assumption for a t-test is that the scale of measurement applied to the data collected follows a continuous or ordinal scale, such as the scores for an IQ test.
When conducting a hypothesis test using an independent measures t statistic a researcher must compute the pooled variance before calculating the estimated standard error?
For an independent-measures t statistic, you typically must compute the pooled variance before calculating the estimated standard error. For a hypothesis test with an independent-measures t, the larger the two sample variances are , the greater the likelihood that you will reject the null hypothesis.
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 does it mean if variance increases?
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 does the independent samples t-test determine quizlet?
What is the purpose of Independent sample t-test? It is used when you have two samples that are not related to one another and you want to see of there is a difference between the means of the two populations form which the samples are drawn.
What is t-test for independent samples?
The Independent Samples t Test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. The Independent Samples t Test is a parametric test. This test is also known as: Independent t Test.
What is the effect of increasing the difference between sample means in a two sample t-test?
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 does an independent t-test measure?
The Independent Samples t Test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. The Independent Samples t Test is a parametric test. This test is also known as: Independent t Test.
What is meant by the difference of the means when talking about an independent samples t-test?
The difference of the means is one mean, calculated from a set of scores, compared to another mean which is calculated from a different set of scores; the independent samples t-test looks for whether the two separate values are different from one another.
Which of the following describes the effect of an increase in the variance of the difference scores?
Q: Which of the following describes the effect of an increase in the variance of the difference scores? Measures of effect size and the likelihood of rejecting the null hypothesis both decrease.
What happens to t-statistic if variance increases?
Doubling the sample size will increase the value of t, (the level of significance I don't understand- still not getting it?), increasing the sample variance will decrease the value of t, and an increase in difference of means will increase the t value.
What increases the t-statistic?
As the difference between the sample data and the null hypothesis increases, the absolute value of the t-value increases.
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.
What happens to T observed when you increase variability of scores?
The variability of the score influences the estimated standard error in the denominator of the t-statistic. As the variability of a scores increases the value of t decreases (becomes closer to zero) and the likelihood of rejecting the H0 decreases.
What is independent t-test in statistics?
The independent t-test, also called the two sample t-test, independent-samples t-test or student's t-test, is an inferential statistical test that determines whether there is a statistically significant difference between the means in two unrelated groups.
Why would you use an independent t-test?
The independent t-test is used when you have two separate groups of individuals or cases in a between-participants design (for example: male vs female; experimental vs control group).
What is the effect size for independent t-test?
The effect size for a t-test for independent samples is usually calculated using Cohen's d.To calculate the effect size, the mean difference is standardized i.e. divided by the standard deviation. However, the standard deviation of the population is not known.
What does the t-value mean in an independent t-test?
The t-value measures the size of the difference relative to the variation in your sample data. Put another way, T is simply the calculated difference represented in units of standard error. The greater the magnitude of T, the greater the evidence against the null hypothesis.
What happens to t-test as sample size increases?
The sample size for a t-test determines the degrees of freedom (DF) for that test, which specifies the t-distribution. The overall effect is that as the sample size decreases, the tails of the t-distribution become thicker.
How does increasing sample size affect t-test?
The smaller the sample size, the greater the influence of the values of individual samples on variance. This variability becomes stable as the sample size increases. If the sample sizes of the groups are different, then this difference in variability may result in different variances.
What is variance in independent t-test?
The variances of the test (dependent) variable in the two populations are equal. This is commonly referred to as the assumption of homogeneity of variance. One of the first steps in using the independent-samples t test is to test the assumption of independence.
How do you interpret independent t-test results?
Independent Samples T Tests Hypotheses If the p-value is less than your significance level (e.g., 0.05), you can reject the null hypothesis. The difference between the two means is statistically significant. Your sample provides strong enough evidence to conclude that the two population means are not equal.
How does the process of testing for a difference in independent samples differ from the process of testing for a difference in dependent samples?
Dependent samples are paired measurements for one set of items. Independent samples are measurements made on two different sets of items. When you conduct a hypothesis test using two random samples, you must choose the type of test based on whether the samples are dependent or independent.
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.
Which of the following accurately describes an independent measures study quizlet?
Which of the following accurately describes an independent-measures study? There is a non-zero mean difference between the two populations being compared.
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.
How does variance affect t-value?
Now, we can see that the t-statistic is inversely proportional to the standard error/variance of the sample population (σ/√n). Higher n leads to smaller standard error that gives higher t-value.
Does variance affect t-test?
The t test can be used with unequal sample sizes. It is usually assumed that the two variances are equal when applying the t test for comparing two means. But even in the cases where the two variances are obviously different the Welch test which approximates a t distribution under the null hypothesis can be applied.