1. Hypothesis testing begins with an assumption about the population parameter. To test the validity of the assumption we gather sample data and determine the difference between the hypothesized value and the actual value. We then judge whether the difference is significant. The smaller the difference the greater the likelihood that our hypothesized value is correct. Determining if the difference is statistically significant helps to remove the opinions that can be incorporated into decisions. Each of us may look at the same difference in values and have a different opinion on whether the difference is significant. Why would opinions vary?
2. One-Tail and Two-Tail Tests

Along with considering one tail and two tail tests we need to be careful not to mix one sample and two sample tests with one tail and two tail tests. A one sample test compares one sample variable to a population parameter. A two sample test compares two sample variables. For example, if we are comparing the weight of chicken eggs to an expected weight (population parameter) we would be using a one sample test. If we are comparing the weight of chicken eggs for chickens that have been fed a certain feed and chickens that have not been fed this feed to see if there is a difference then we are performing a two sample test.

A one tail or two tail test is determined by what we want to know. A one sample test looks for either less than or greater than comparisons while a two tail test looks to determine if the sample or samples are equal.

It is possible that we can the following combinations:

- One sample test that is one tailed.

- One sample test that is two tailed.

- Two sample test that is one tailed.

Two sample test that is two tailed

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