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CandleFocus Glossary Editor
A Z-test is a type of hypothesis test used in statistics to determine if two groups have different population means. A hypothesis test is conducted using sample data to determine the likelihood of measuring an effect if the experiment was conducted multiple times. In a Z-test, the sample data is compared against a null hypothesis stating that there is not a statistically significant difference between the two population means.
The main advantage of a Z-test is that it is more powerful than the t-test. This means that it has better detection power and can detect smaller differences. In order to do a Z-test, however, the standard deviation of the populations needs to be known. Furthermore, Z-tests are typically used when the sample size is larger than 30.
To conduct a Z-test, the researcher first identifies the sample set and calculates a z-score from the test statistic. The researcher then looks up the critical Z-score in a Z-table to find the level of confidence that either the null or alternative hypothesis is correct. The confidence levels are usually identified as 0.90 (90% confidence), 0.95 (95% confidence) and 0.99 (99% confidence).
The main results of the Z-test are p-values and confidence intervals, which describe how confident the researcher can be that the difference between the two populations means is statistically significant. The smaller the p-value, the stronger the evidence that the difference between the two population means is statistically significant. If the p-value is smaller than a predetermined level, then it is considered statistically significant, and the null hypothesis can be rejected.
In summary, a Z-test is a type of hypothesis test that is used to determine if two groups have different population means when the sample size is larger than 30 and the standard deviation is known. The main advantage of using a Z-test is that it is more powerful than the t-test and can detect smaller differences. The main results of the Z-test are the p-values and confidence intervals, which provide the chances of making a Type I error, or rejecting the null hypothesis incorrectly.
The main advantage of a Z-test is that it is more powerful than the t-test. This means that it has better detection power and can detect smaller differences. In order to do a Z-test, however, the standard deviation of the populations needs to be known. Furthermore, Z-tests are typically used when the sample size is larger than 30.
To conduct a Z-test, the researcher first identifies the sample set and calculates a z-score from the test statistic. The researcher then looks up the critical Z-score in a Z-table to find the level of confidence that either the null or alternative hypothesis is correct. The confidence levels are usually identified as 0.90 (90% confidence), 0.95 (95% confidence) and 0.99 (99% confidence).
The main results of the Z-test are p-values and confidence intervals, which describe how confident the researcher can be that the difference between the two populations means is statistically significant. The smaller the p-value, the stronger the evidence that the difference between the two population means is statistically significant. If the p-value is smaller than a predetermined level, then it is considered statistically significant, and the null hypothesis can be rejected.
In summary, a Z-test is a type of hypothesis test that is used to determine if two groups have different population means when the sample size is larger than 30 and the standard deviation is known. The main advantage of using a Z-test is that it is more powerful than the t-test and can detect smaller differences. The main results of the Z-test are the p-values and confidence intervals, which provide the chances of making a Type I error, or rejecting the null hypothesis incorrectly.