Which set of characteristics will produce the smallest value for the estimated standard error?
Answer and Explanation: The scenario that will result in the smallest value for the standard error is option A: A large sample size and a small sample variance.
Which factor will increase the chances of rejecting the null hypothesis?
When we increase the sample size, decrease the standard error, or increase the difference between the sample statistic and hypothesized parameter, the p value decreases, thus making it more likely that we reject the null hypothesis.
Which of the following is an accurate definition for the power of a statistical test?
Which of the following is an accurate definition for the power of a statistical test? The probability of rejecting a false null hypothesis.
What is assumed by the homogeneity of variance assumption quizlet?
The homogeneity of variance assumption states that the two population variances are equal.
Which set of sample characteristics is most likely to produce a significant t statistic?
Which set of sample characteristics is most likely to produce a significant t statistic? A large sample size and a small sample variance.
When n is small less than 30 how does the shape of the T distribution?
For practical purposes, the shape of the t-distribution is identical to the normal distribution when sample size is large. However, when sample sizes are small (below 30 subjects), the shape of the t-distribution is flatter than that of the normal distribution, and the t-distribution has greater area under the tails.
Why reject null hypothesis when P-value is small?
A crucial step in null hypothesis testing is finding the likelihood of the sample result if the null hypothesis were true. This probability is called the p value . A low p value means that the sample result would be unlikely if the null hypothesis were true and leads to the rejection of the null hypothesis.
How do you increase statistical significance?
Increase the significance level (alpha), Reduce measurement error by increasing the precision and accuracy of your measurement devices and procedures, Use a one-tailed test instead of a two-tailed test for t tests and z tests.
Under what circumstances will the distribution of sample means be normal?
In order for the distribution of sample means to be normal, it must be based on samples of at least n = 30 scores.
Under what circumstance should the chi square statistic not be used?
Another consideration one must make is that the chi-square statistic is sensitive to sample size. Most recommend that chi-square not be used if the sample size is less than 50, or in this example, 50 F2 tomato plants. If you have a 2×2 table with fewer than 50 cases many recommend using Fisher's exact test.
How does increasing sample variance affect the likelihood that a hypothesis test will be significant?
In a hypothesis test, a large value for the sample variance increases the likelihood that you will find a significant treatment effect. In an analysis of variance, MStotal = MSbetween + MSwithin. The larger the differences among the sample means, the larger the numerator of the F-ratio will be.
Why is homogeneity of variance important for the independent measures t-test?
Homogeneity of variance essentially makes sure that the distributions of the outcomes in each group are comparable and similar. If independent groups are not similar in this regard, superfluous findings can be yielded.
What would most likely produce a significant t-test result?
Which set of sample characteristics is most likely to produce a significant t statistic? A large sample size and a small sample variance.
What value is expected for the t statistic when the null hypothesis is true?
0 If the sample data equals the null hypothesis precisely, the t-test produces a t-value of 0.
How does sample size affect T value?
t-Distributions and Sample Size 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 sample size affect t-distribution?
As explained above, the shape of the t-distribution is affected by sample size. As the sample size grows, the t-distribution gets closer and closer to a normal distribution. Theoretically, the t-distribution only becomes perfectly normal when the sample size reaches the population size.
Is a smaller p-value more significant?
A p-value measures the probability of obtaining the observed results, assuming that the null hypothesis is true. The lower the p-value, the greater the statistical significance of the observed difference. A p-value of 0.05 or lower is generally considered statistically significant.
How small of an can you choose and still have sufficient evidence to reject the null hypothesis?
When your p-value is less than or equal to your significance level, you reject the null hypothesis. The data favors the alternative hypothesis.
What is the minimum sample size for statistical significance?
“A minimum of 30 observations is sufficient to conduct significant statistics.” This is open to many interpretations of which the most fallible one is that the sample size of 30 is enough to trust your confidence interval.
Why is effect size important in statistics?
Effect size tells you how meaningful the relationship between variables or the difference between groups is. It indicates the practical significance of a research outcome. A large effect size means that a research finding has practical significance, while a small effect size indicates limited practical applications.
Under which circumstance will the distribution of sample means be approximately normal quizlet?
Under what circumstances is the distribution of sample means normal? If the sample size is greater than 30. If the population is normal or if the sample size is greater than 30. It is always normal.
Which samples size will give a smaller standard error of the mean?
larger The standard error is also inversely proportional to the sample size; the larger the sample size, the smaller the standard error because the statistic will approach the actual value.
Under what circumstances a chi-square test would be appropriate?
Market researchers use the Chi-Square test when they find themselves in one of the following situations: They need to estimate how closely an observed distribution matches an expected distribution. This is referred to as a “goodness-of-fit” test. They need to estimate whether two random variables are independent.
Under what conditions chi-square test is applicable?
A chi-square test is used to help determine if observed results are in line with expected results, and to rule out that observations are due to chance. A chi-square test is appropriate for this when the data being analyzed is from a random sample, and when the variable in question is a categorical variable.
Why is a smaller variance better?
A small variance indicates that the data points tend to be very close to the mean, and to each other. A high variance indicates that the data points are very spread out from the mean, and from one another.
Do you want homogeneity of variance to be significant?
The assumption of homogeneity is important for ANOVA testing and in regression models. In ANOVA, when homogeneity of variance is violated there is a greater probability of falsely rejecting the null hypothesis.
What happens if Levene’s test is significant?
If the Levene's Test is significant (the value under "Sig." is less than . 05), the two variances are significantly different. If it is not significant (Sig. is greater than . 05), the two variances are not significantly different; that is, the two variances are approximately equal.
How do you determine statistical significance?
How to Calculate Statistical Significance
- Determine what you'd like to test.
- Determine your hypothesis.
- Start collecting data.
- Calculate Chi-Squared results.
- Calculate your expected results.
- See how your results differ from what you expected.
- Find your sum.
- Report on statistical significance to your teams.
Sep 30, 2021
What is a statistically significant result?
A result of an experiment is said to have statistical significance, or be statistically significant, if it is likely not caused by chance for a given statistical significance level. Your statistical significance level reflects your risk tolerance and confidence level.
What does a small p-value mean?
A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance. A non-significant result, leading us not to reject the null hypothesis, is evidence that the null hypothesis is true.