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To really understand significance, we need to talk a little more about *p-values* and *statistical tests*. These are the tools researchers use to determine if their results are likely to be due to chance. A *p-value* represents the probability of obtaining results as extreme as, or more extreme than, those observed in the study, assuming that there is no real effect. The lower the p-value, the less likely the results are due to chance, and the more likely they reflect a genuine effect. The alpha level, which is the predetermined threshold (usually 0.05), is the standard used to judge statistical significance. If the p-value is less than the alpha level, the results are considered statistically significant. If the p-value is greater than the alpha level, the results are *not* considered statistically significant. In this case, the results are inconclusive. This does not mean the absence of an effect. It could mean the sample size was too small, the study was not well designed, or there was a high degree of variability in the data.
