Final answer:
Statistical significance is used to assess how likely it is that two or more samples came from the same population. It is used in various types of tests, such as the Student's t-test for comparing means or tests for comparing proportions, to evaluate the probability that the observed differences are due to natural variability rather than actual differences in the population.
Step-by-step explanation:
The term that allows us to determine how likely it is that two or more samples came from the same population is c) Statistical significance. This term is associated with hypothesis testing, where we compare two or more groups to see if the differences observed in the samples are likely to reflect true differences in the populations, or if they are due to random variation.
In cases such as matched or paired samples or when we compare two independent means with either known or unknown population standard deviations and variances, we often use Student's t-test. On the other hand, when comparing two population proportions, we generally rely on a normal distribution to determine the statistical significance.
For example, if we have two populations, one following a powder diet and another following a liquid diet, with known population standard deviations, and we want to test if the liquid diet yields a higher mean weight loss, we would use an independent group means test. If we are looking at the proportions of males and females passing their driver's test on the first attempt and want to know if there is a significant difference between these proportions, we would use a two-proportions test.
To determine if the correlation between two variables is significant, which would justify using the sample data's regression line as the best estimate of the population's regression line, we examine a scatter plot and test the significance of the correlation coefficient.