This is an important distinction; unfortunately, statistical significance is often misunderstood and misused in organizations today. He also advises organizations on their data and data quality programs.
Consider the example of a marketing campaign. This is called a sampling error , something you must contend with in any test that does not include the entire population of interest. Redman notes that there are two main contributors to sampling error: the size of the sample and the variation in the underlying population. Sample size may be intuitive enough.
Think about flipping a coin five times versus flipping it times. Of course, showing the campaign to more people costs more, so you have to balance the need for a larger sample size with your budget. Variation is a little trickier to understand, but Redman insists that developing a sense for it is critical for all managers who use data.
Consider the images below. Each expresses a different possible distribution of customer purchases under Campaign A. In the chart on the left with less variation , most people spend roughly the same amount of dollars.
Compare that to the chart on the right with more variation. Here, people vary more widely in how much they spend. The average is still the same, but quite a few people spend more or less. This means that the evidence published in scientific journals is biased towards studies that find effects. A study published in Science by a team from Stanford University who investigated survey-based experiments funded by the National Science Foundation found that nearly two-thirds of the social science experiments that produced null results were filed away, never to be published.
Mehler is the co-author of a recent article published in the Journal of European Psychology Students about appreciating the significance of non-significant findings.
It could mean that the null hypothesis is true — there really is no effect. But it could also indicate that the data are inconclusive either way. This, he adds, is a particular problem for students and early career researchers, whose limited resources often constrain them to small sample sizes. One solution is to collaborate with other researchers to collect more data.
In psychology, the StudySwap website is one way for researchers to team up and combine resources. A one-tailed hypothesis is where you predict a specific direction of the difference higher, lower or relationship positive, negative between the two groups or variables of interest.
Therefore, with a one-tailed test, while your alpha value stays the same, you halve your p value because you are focusing on one specific direction only.
On the other hand, a two-tailed hypothesis is where you do not predict a specific direction of the difference or relationship and as such with a two-tailed test you keep the p value as a whole number. Two-tailed tests are more widely used in research compared to one-tailed tests.
Statistical power refers to the probability that the statistical test you are using will correctly reject a false null hypothesis. Statistical power is increased by having an adequate sample size. Generally, if the alternate hypothesis is true and there is a difference or relationship to be observed, then with a larger sample the chances of seeing this difference or relationship will increase.
If you see a difference or relationship between two small groups, you could reasonably expect that the difference or relationship would increase in prominence if the groups became larger.
An effect size is a numerical index of how much your dependent variable of interest is affected by the independent variable, and determines whether the observed effect is important enough to translate to the real world. Therefore, effect sizes should be interpreted alongside your significance results. The other effect size is eta-squared, with measures the strength of the relationship between two variables.
For eta-squared, a score of. Both of these effect sizes can be calculated by hand, or you can ask for it to be calculated for you as part of statistics software. Sign Up. Get Started.
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