# Alternative Hypothesis (Ha)

**An alternative hypothesis** (Ha) states that there is a statistically significant relationship between the two variables. The **null hypothesis** says there is no statistical relationship between the two variables. There are many types of hypotheses in statistics. A statistical hypothesis is a working statement that is logically consistent with data. A hypothesis should not be taken as true or false.

An alternative hypothesis (HA) is a statement that is used in statistical inference experiments. It contradicts the null hypothesis and**, denoted H **_{a} . It can also be said that it is an alternative to null. Hypothesis testing is where a researcher tests alternative theories. This statement is true in the researcher’s view. It ultimately rejects the null and replaces it with another assumption. The researchers predict the difference between the two variables so that the pattern of data seen in the test does not result from chance.

To understand an **alternative hypothesis** (HA) (also known as an *alternative hypothesis*), you must first understand the null hypothesis. A hypothesis is a working sentence. Statistics are interested in verifying whether a working assertion (the null hypothesis), is true or false. These working statements are usually things that can be expected to be true. They may include some kind of historical value, known fact, or fact. The term “null” is also a synonym for “no change”. The null hypothesis is historical and gives you what you would expect.

When performing a two-tailed hypotheses test, the alternative hypothesis is that the population parameter is not equal to the null hypothesis value. If the Ha is H m 0, then the test can detect both differences greater than or less than the null value.

One-tailed alternative hypotheses can only test for a difference in one direction. For example, H _{A} m > 0 cannot test for differences greater than zero.

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