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What does "statistical significance" really mean?

Many researchers get very excited when they have discovered a "statistically significant" finding, without really understanding what it means. When a statistic is significant, it simply means that you are very sure that the statistic is reliable. It doesn't mean the finding is important or that it has any decision-making utility.

For example, suppose we give 1,000 people an IQ test, and we ask if there is a significant difference between male and female scores. The mean score for males is 98 and the mean score for females is 100. We use an independent groups t-test and find that the difference is significant at the .001 level. The big question is, "So what?". The difference between 98 and 100 on an IQ test is a very small difference...so small, in fact, that its not even important.

Then why did the t-statistic come out significant? Because there was a large sample size. When you have a large sample size, very small differences will be detected as significant. This means that you are very sure that the difference is real (i.e., it didn't happen by fluke). It doesn't mean that the difference is large or important. If we had only given the IQ test to 25 people instead of 1,000, the two-point difference between males and females would not have been significant.

Significance is a statistical term that tells how sure you are that a difference or relationship exists. To say that a significant difference or relationship exists only tells half the story. We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship? After finding a significant relationship, it is important to evaluate its strength. Significant relationships can be strong or weak. Significant differences can be large or small. It just depends on your sample size.

Many researchers use the word "significant" to describe a finding that may have decision-making utility to a client. From a statistician's viewpoint, this is an incorrect use of the word. However, the word "significant" has virtually universal meaning to the public. Thus, many researchers use the word "significant" to describe a difference or relationship that may be strategically important to a client (regardless of any statistical tests). In these situations, the word "significant" is used to advise a client to take note of a particular difference or relationship because it may be relevant to the company's strategic plan. The word "significant" is not the exclusive domain of statisticians and either use is correct in the business world. Thus, for the statistician, it may be wise to adopt a policy of always referring to "statistical significance" rather than simply "significance" when communicating with the public.

One-Tailed and Two-Tailed Significance Tests

One important concept in significance testing is whether you use a one-tailed or two-tailed test of significance. The answer is that it depends on your hypothesis. When your research hypothesis states the direction of the difference or relationship, then you use a one-tailed probability. For example, a one-tailed test would be used to test these null hypotheses: Females will not score significantly higher than males on an IQ test. Blue collar workers are will not buy significantly more product than white collar workers. Superman is not significantly stronger than the average person. In each case, the null hypothesis (indirectly) predicts the direction of the difference. A two-tailed test would be used to test these null hypotheses: There will be no significant difference in IQ scores between males and females. There will be no significant difference in the amount of product purchased between blue collar and white collar workers. There is no significant difference in strength between Superman and the average person. The one-tailed probability is exactly half the value of the two-tailed probability.

There is a raging controversy (for about the last hundred years) on whether or not it is ever appropriate to use a one-tailed test. The rationale is that if you already know the direction of the difference, why bother doing any statistical tests. While it is generally safest to use a two-tailed tests, there are situations where a one-tailed test seems more appropriate. The bottom line is that it is the choice of the researcher whether to use one-tailed or two-tailed research questions.

Procedure Used to Test for Significance

Whenever we perform a significance test, it involves comparing a test value that we have calculated to some critical value for the statistic. It doesn't matter what type of statistic we are calculating (e.g., a t-statistic, a chi-square statistic, an F-statistic, etc.), the procedure to test for significance is the same.

  1. Decide on the critical alpha level you will use (i.e., the error rate you are willing to accept).
  2. Conduct the research.
  3. Calculate the statistic.
  4. Compare the statistic to a critical value obtained from a table.

If your statistic is higher than the critical value from the table:

  • Your finding is significant.
  • You reject the null hypothesis.
  • The probability is small that the difference or relationship happened by chance, and p is less than the critical alpha level (p < alpha ).

If your statistic is lower than the critical value from the table:

  • Your finding is not significant.
  • You fail to reject the null hypothesis.
  • The probability is high that the difference or relationship happened by chance, and p is greater than the critical alpha level (p > alpha ).

Modern computer software can calculate exact probabilities for most test statistics. If you have an exact probability from computer software, simply compare it to your critical alpha level. If the exact probability is less than the critical alpha level, your finding is significant, and if the exact probability is greater than your critical alpha level, your finding is not significant. Using a table is not necessary when you have the exact probability for a statistic.

 

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