Redman advises that you should plot your data and make pictures like these when you analyze the data. The graphs will help you get a feel for variation, the sampling error, and, in turn, the statistical significance. The significance level is an expression of how rare your results are, under the assumption that the null hypothesis is true.
Setting a target and interpreting p-values can be dauntingly complex. Redman says it depends a lot on what you are analyzing. Then you collect your data, plot the results, and calculate statistics, including the p-value, which incorporates variation and the sample size. If you get a p-value lower than your target, then you reject the null hypothesis in favor of the alternative. Again, this means the probability is small that your results were due solely to chance.
There is also a formula in Microsoft Excel and a number of other online tools that will calculate it for you. For example, if a manager runs a pricing study to understand how best to price a new product, he will calculate the statistical significance — with the help of an analyst, most likely — so that he knows whether the findings should affect the final price.
If the p-value comes in at 0. But what if the difference were only a few cents? But even if it had a significance level of 0. In this case, your decision probably will be based on other factors, such as the cost of implementing the new campaign. Closely related to the idea of a significance level is the notion of a confidence interval. To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:. The boundaries of this confidence interval around the difference also provide a way to see what the upper and lower bounds of the improvement could be if we were to go with landing page A.
To declare practical significance, we need to determine whether the size of the difference is meaningful. In our conversion example, one landing page is generating more than twice as many conversions as the other. As we might expect, the likelihood of obtaining statistically significant results increases as our sample size increases.
For example, in analyzing the conversion rates of a high-traffic ecommerce website, two-thirds of users saw the current ad that was being tested and the other third saw the new ad. Conversely, small sample sizes say fewer than 50 users make it harder to find statistical significance; but when we do find statistical significance with small sample sizes, the differences are large and more likely to drive action.
Some standardized methods express differences, called effect sizes , which help us interpret the size of the difference. Here, too, the context determines whether the difference warrants action. Skip to content. What Does Statistically Significant Mean? Jeff Sauro, PhD. October 21, Consider these two important factors. This means we can determine if something is actually working better than leaving things alone.
Nutritionists do this all the time when testing new rations; pharmaceutical companies do this when testing new drugs or vaccines. Veterinarians, and more likely research scientists, may use this to determine if a new type of surgery or expensive treatment is worthwhile. While knowing how to perform these tests is important for researchers, from a practical standpoint remember two important factors: sampling error andprobability.
This is sampling error. Probability is just that, the likelihood of something actually happening. The higher the probability of a specific event or outcome, the more likely it is to happen.
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