Answer:
Sample size has a great affect on the hypothesis. Greater the sample size, greater the power of test and vice versa. Level of significance indicates about the hypothesis whether it is true or false. If the difference between actual value and hypothesis is large, so this hypothesis is considered as false.
Explanation:
For example, if a scientist performed an experiment on fertilizer and make a hypothesis that fertilizer enhance plants growth and yield. If the difference between the actual value and hypothesis is small so this hypothesis is accepted and verified again and again.