In statistical hypothesis testing, the **alternative hypothesis** is one of the proposed proposition in the hypothesis test. In general the goal of hypothesis test is to demonstrate that in the given condition, there is sufficient evidence supporting the credibility of alternative hypothesis instead of the exclusive proposition in the test (null hypothesis).[1] It is usually consistent with the **research hypothesis** because it is constructed from literature review, previous studies, etc. However, the research hypothesis is sometimes consistent with the null hypothesis.

In statistics, alternative hypothesis is often denoted as **Ha** or **H1**. Hypotheses are formulated to compare in a statistical hypothesis test.

In the domain of inferential statistics two rival hypotheses can be compared by explanatory power and predictive power.

## Alternative Hypotheses: Main Ideas!!!

## Types of an alternative hypothesis

There are a few types of alternative hypothesis to consider, including:

**One-tailed directional**

In a one-tailed directional test, the alternative hypothesis only tests one direction. For example, the test can only uncover if the differences are greater than or less than zero, but not both at once. If the researcher suspects the difference is less, then they describe the test as left-tailed. If the researcher proposes the difference is greater than zero, then they describe the test as right-tailed.

**Two-tailed or non-directional**

In a two-tailed or non-directional test, the alternative hypothesis claims its parameters don’t equal the null hypothesis value. This means the two-tailed directional test states there are differences present that are greater than and less than the null value. It’s important to note it only claims a difference exists, but it doesn’t show a direction of difference between the null and alternative hypotheses.

## What is an alternative hypothesis?

Statisticians and researchers use alternative and null hypotheses when conducting research in a variety of industries, including:

## Examples of an alternative hypothesis

Here are two examples of an alternative hypothesis:

**One-tailed example**

Then, they create an alternative hypothesis. An alternative hypothesis claims the opposite, saying, “Employee candidates with no work experience receive as many interview invitations as those with a minimum of four years.” Here, the researcher designs a test to determine how many years of work experience people who receive an interview request have. They set the parameters of the research and work to reject the initial claim. If research doesn’t prove the alternative hypothesis to be true, they say they can’t reject the null hypothesis. If enough evidence is present to reject it, then they can conclude they can reject it.

**Two-tailed example**

High school students in an advanced classroom complete a test. A school researcher claims the additional training will cause the average classroom grades to be higher than the states average. They create the null hypothesis that says, “The average test grades in the advanced classroom are higher than the states average of 1,000 points.”

The researcher also creates the alternative hypothesis that claims, “The advanced learning program has little effect on students grades, and there will be no correlation between test scores.” This is an example of a two-sided hypothesis because the researcher wants to identify if the scores are lower or higher than the state average. They then work to either accept or reject the initial null hypothesis.

## Comparing alternative hypothesis to null hypothesis

Here are a few comparisons between null and alternative hypotheses to help you understand them better:

**Descriptions**

You use symbols to describe the alternative and null hypotheses. The alternative hypothesis uses the symbol Ha, while the null hypothesis uses the symbol Ho. After completing your research, you decide whether to reject or decline to reject the Ho.When you decline to reject the Ho, you aren’t saying the statement is true. Instead, that means you can’t disprove the hypothesis.

Both hypotheses use greater than, less than or equal to in their claim. For instance, you can have a null hypothesis that says the melting point of aluminum is equal to 1,221 degrees, and your alternative hypothesis may say the melting point is greater than 1,221 degrees.

**Implications**

The alternative hypothesis includes a statement that opposes the null hypothesis. A researcher performs additional research to find flaws in the null hypothesis. Following the research, which uses the alternative hypothesis as a guide, they may decide whether they have enough evidence to reject the null hypothesis. They also may reject the alternative hypothesis and choose a different one to test.

**Assumptions**

A null hypothesis is a statement you try to reject, which means a null assumes something is true. For example, a null hypothesis may say the temperature has no impact on crime rates in a certain city. The alternative hypothesis assumes this is false, meaning it may claim the weather has a positive or negative impact on crime rates. Because you want to disprove the null hypothesis, and the alternative assumes the null is wrong, this means you use the alternative to gather data to support the experiment.

**Purposes**

An alternative and null hypothesis include statements with the same purpose of providing the researcher with a basic guideline. The researcher uses the statement from each hypothesis to guide their research. All research findings describe how the data either rejects or fails to reject the hypothesis claim.

**Tests**

Although it’s not always possible to test research problems, you can test an alternative and null hypothesis. You may want to describe both in a way that allows you to gather information when researching them. Both alternative and null hypotheses have one- and two-sided testing capabilities, and they can apply to a wide range of research opportunities.