Most papers that employ Difference-in-Differences estimation use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent, leading to serious over-estimation of t-statistics and significance levels.
Since it is considered that the only functional difference between groups is the level of experimental condition(s) received, researchers can strongly infer cause-and-effect relationships from statistically significant associations between experimental condition(s) and health consequences.
A review in 2013 of studies on individual culture media came to the result that the G3 medium confers a significantly higher live birth rate and a lower risk of low birthweight than the Sydney IVF medium.
For example, if OLS is performed on a heteroscedactic data set, yielding biased standard error estimation, a researcher might fail to reject a null hypothesis at a given significance level, when that null hypothesis was actually uncharacteristic of the actual population (making a type II error).
Statistical significance, the extent to which a result is unlikely to be due to chance alone
Used as such, it is a test of statistical significance.
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However, a similar analysis by American economists Garett Jones and Schneider (2006) showed that IQ is a statistically significant explanatory variable of economic growth.
The Newman–Keuls or Student–Newman–Keuls (SNK) method is a stepwise multiple comparisons procedure used to identify sample means that are significantly different from each other.
In the 1970s, a thorough examination of the statistical significance of various sites seemingly on a linear path, conducted by the Royal Statistical Society and Simon Broadbent, attempted to determine the normality of the distribution of these megalithic sites.