Significance testing

What Significance testing is

Significance testing is a statistical technique used to determine whether a pattern or a relationship between variables in a data set is due to chance or is likely to be a real effect. It involves comparing an observed result to a hypothetical distribution (usually a normal distribution) in order to determine if the observed result is statistically significant. Significance testing is often used in hypothesis testing and involves the following steps:

  1. Formulate a hypothesis – This is usually a statement about the population being studied. It is usually about the relationship between two variables in the population.

  2. Identify a test statistic – The test statistic is a summary of the data that will be used to evaluate the hypothesis. This is usually a measure of the difference between the observed data and the expected value under the null hypothesis.

  3. Choose a level of significance – This is the probability of rejecting the null hypothesis when it is true. It is usually set at 0.05 or 0.01.

  4. Calculate the test statistic – This involves calculating the test statistic from the sample data.

  5. Compare the test statistic to the critical value – This is the value that separates what is considered to be statistically significant from what is not.

  6. Make a decision – If the test statistic is greater than the critical value, then the null hypothesis is rejected and the alternative hypothesis is accepted. If the test statistic is less than the critical value, then the null hypothesis is accepted.

Examples

  1. A retail business wants to compare the average sales in two different weeks. They can use significance testing to determine whether the difference in the sales is significant or merely due to chance.

  2. An educational institution wants to determine whether there is a statistically significant difference between the test results of two different classes. They can use significance testing to evaluate the differences.

  3. A hospital wants to compare the mortality rates of two different treatments. They can use significance testing to determine whether the differences in the mortality rates are significant or simply due to chance.

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