normal, SRS, independent normal, SRS, independent
In ANOVA, what does F=1 mean? What are the differences between a two sample t-test and ANOVA hypothesis testing? When would you use ANOVA at your place of employment, in your education, or in politics?
advantage
Independence of the two samples means that the choosing of the first sample did not influence the choosing of the other sample, and vice versa. For example, if you were comparing running speed in two different brands of running shoes, you could look at two samples of people running a 100 m dash -- one sample of people running in Brand A and one sample of people running in Brand B. If those two groups were picked independently of one another, these samples would be independent. If, instead, you had the same group of people run the race twice (once in each brand of shoe), these samples would be dependent. Samples that are not independent are said to be "correlated", "interdependent", or "dependent". Because the two samples are correlated, you might get incorrect findings for your statistical study. For example, say you want to compare the heights of boys and girls. If you chose the samples by choosing a girl for the girl sample, then choosing her brother for the boy sample, your statistical analyses might be misleading if you didn't account for the fact that tall girls are more likely to have tall brothers, and short girls are more likely to have short brothers. By choosing siblings for the two groups, you have made the two samples not independent of one another. If the independence assumption is violated, you have to do a special type of statistical test. For example, instead of doing a two-sample t-test, you would have to do a paired t-test.
Yes, it is. The one sample t-test is a study of the parameter population-mean. You can also use the t-test to test for the difference between two population means (both parameters).
normal, SRS, independent normal, SRS, independent
No.
two underlying assumptions you make when preparing the Income Statement and Balance Sheet
Assumptions can fall into two categories: explicit assumptions, which are consciously stated or believed, and implicit assumptions, which are subconscious beliefs taken for granted. Explicit assumptions are those that are openly expressed and acknowledged, while implicit assumptions are underlying beliefs that may not be overtly stated but still influence thoughts and actions.
Calibration error (the equipment gives the incorrect result) and false assumptions (the sample is uniform and solid).
Two scientists may have different underlying assumptions that lead them to different conclusions about the same data.
A matched test is appropriate only if the two variables are measures taken for the same subject.
Urine Sample, Hair Sample, Blood sample. Most common is Urine, although hair can hold up to two months of history per inch.
In ANOVA, what does F=1 mean? What are the differences between a two sample t-test and ANOVA hypothesis testing? When would you use ANOVA at your place of employment, in your education, or in politics?
The differences in test scores, or predictions from those scores, between two or more subgroups of the population that are matched on the underlying construct being measured.
The Independent Samples T Test compares the mean scores of two groups on a given variable.
If they are not matched pairs, it does not really matter. If the combined sample size is fixed (because of costs, say) then it is better to have a larger sample where more variability is expected.