# Statistical Significance

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## Statistical Significance

Statistical significance is used by market researchers to ensure their findings have not occurred by chance, and are reliable. For a research finding to be statistically significant, the researcher must show it is at least 95% probable, allowing for an error margin of 5%; this means that if the study was repeated 100 times, a minimum of 95 test results would be the same. In market research, it is highly unlikely that a research study will include all of a target population or be without bias, which makes statistical significance important in demonstrating the reliability of the findings.

To calculate the statistical significance of a finding, the researcher will conduct either a T-test or a Z-test. A T-test assesses whether the findings were a result of chance by checking if two independent groups have the same mean. A Z-test checks the significance of a finding by checking if two independent groups have an equal population proportion. If the tests prove that the mean or proportion are equal, the findings will not be statistically significant.

An important factor that could prevent a finding from being statistically significant is sampling error. Sampling error is when the participants selected for a research study are not representative of the whole target population. In this case, the results will not reflect the whole population correctly, meaning there is no statistical significance.

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