Returns the one-tailed P-value of a Z-test with standard distribution.
Common questions about the Z.TEST formula include:
1. What is the Z.TEST formula?
2. How is the Z.TEST formula used?
3. What does the Z.TEST formula tell us?
The Z.TEST formula can be used appropriately to calculate the statistical significance in comparing two datasets by determining if the difference between the two is simply due to chance.
The Z.TEST formula can be commonly mistyped as ""Z test,"" ""Z-test,"" or ""Z.test.""
Common ways the Z.TEST formula is used inappropriately include using it as a general hypothesis test, or using it to compare means without first checking that the datasets are normally distributed.
Common pitfalls when using the Z.TEST formula include not considering the effect of outliers in the dataset, or forgetting to adjust the degrees of freedom when comparing two datasets of different sizes.
Common mistakes when using the Z.TEST Formula include using the wrong value of z, using the wrong standard deviation, or not considering the effect of outliers.
Common misconceptions people might have with the Z.TEST Formula include believing it is the same as a t-test or correlational analysis, believing it is related to ANOVA, mistaking a one-tailed test for a two-tailed test, or assuming it can compare means of more than two datasets.
Also see ZTEST()
Common questions about the Z.TEST formula include:
1. What is the Z.TEST formula?
2. How is the Z.TEST formula used?
3. What does the Z.TEST formula tell us?
The Z.TEST formula can be used appropriately to calculate the statistical significance in comparing two datasets by determining if the difference between the two is simply due to chance.
The Z.TEST formula can be commonly mistyped as ""Z test,"" ""Z-test,"" or ""Z.test.""
Common ways the Z.TEST formula is used inappropriately include using it as a general hypothesis test, or using it to compare means without first checking that the datasets are normally distributed.
Common pitfalls when using the Z.TEST formula include not considering the effect of outliers in the dataset, or forgetting to adjust the degrees of freedom when comparing two datasets of different sizes.
Common mistakes when using the Z.TEST Formula include using the wrong value of z, using the wrong standard deviation, or not considering the effect of outliers.
Common misconceptions people might have with the Z.TEST Formula include believing it is the same as a t-test or correlational analysis, believing it is related to ANOVA, mistaking a one-tailed test for a two-tailed test, or assuming it can compare means of more than two datasets.
Also see ZTEST()