To determine whether a result is statistically significant, a researcher would have to calculate a p-value, which is the probability of observing an effect given that the null hypothesis is true. The null hypothesis is rejected if the p-value is less than the significance or α level.
Just because you get a low p-value and conclude a difference is statistically significant, doesn’t mean the difference will automatically be important. To declare practical significance, we need to determine whether the size of the difference is meaningful. If your data is more significance, it means the less likely your data is true or have passed the null hypothesis. In order for a null hypothesis to be rejected it has to be tested to show that there is no difference, which can contradict the research hypothesis (Frankfort-Nachmias & Leon-Guerrero, 2015). P-value needs to be less or equal to 05, .01 or .001 to reduce the sample errors or to reject null hypothesis.
