What can you say about the statistical significance of coefficients?
The statistical significance of coefficients indicates the reliability and importance of the relationship between independent variables and the dependent variable in a statistical model. It helps determine if the relationship observed in the model is likely to be a true representation of the population.
When a coefficient is statistically significant, it means that there is a low probability that the relationship between the variable and the outcome occurred by chance alone. Conversely, when a coefficient is not statistically significant, it suggests that the relationship observed could have happened due to random variation.
The statistical significance of coefficients is typically assessed using hypothesis testing, where a null hypothesis assumes that the coefficient is zero (no relationship), and an alternative hypothesis assumes that the coefficient is non-zero (there is a relationship). The significance is determined based on the p-value associated with the coefficient, which measures the probability of obtaining that coefficient under the null hypothesis.
A commonly used threshold for statistical significance is a p-value of 0.05 or lower. If the p-value is less than this threshold, the coefficient is considered statistically significant, indicating a strong evidence of a relationship. However, it is important to note that statistical significance does not necessarily imply the size of the effect or its practical significance. It is also subject to the assumptions and limitations of the statistical model being used.