One Tailed vs Two Tailed Hypothesis: Know Uses and Differences

13 May 2025 25 Views Share

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As a college student, you must be confused about the tail one and the tail two tests; you must wonder where you will use them and how you will use them in your work. Therefore, when researching the statistical test, whether you're doing it on ANOVA or on others, a regression or some test, you are given a p-value in the output. Choosing one or two-tailed hypothesis tests is one of the crucial decisions you must make for your statistical analysis. But before choosing between them, you need to understand what a one-tailed and a two-tailed hypothesis test is, and as the topics say, one-tailed vs two-tailed hypotheses, you need to know the difference between them. This blog will help you in solving your confusion and help you in choosing your hypothesis test.

What Tails Do In Hypothesis

Before discussing these tests, we need to check some background to understand the tales used in the tests properly. Mainly, the hypothesis test takes all the sample data and converts a single value, which is known as a test statistic. For example, the t-test calculates the t-value, and the F-test, like ANOVA, generates the F-value.

On the other hand, the chi-square test of independence and some distribution tests produce chi-square values. These are all the test values of the statistics. All these tests follow the sample distribution. So these were the uses of different tests in the hypothesis. Now, let's come to the main topic, which is the difference between one-tailed and two-tailed. Firstly, let's discover them both separately.

What is a One-Tail Hypothesis? 

The one-tailed hypothesis is also known as the directional and one-sided tailed test. Do you know why it's called that? Because in this, you can only test for effects in one direction. These hypotheses are mainly used in statistics to find outcomes. Most students get confused in this one, but don't worry; you can seek statistics assignment help from experts. The expert will provide a good-quality project that will improve your scoring. Whenever you perform this test, the entire percentage level goes to the end of one tail of the distribution. In the one-tailed hypothesis, you get two positions for the null and alternative hypotheses, according to where you place the critical region.

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Use of One-Tailed Hypotheses

So, when is a one-tailed test used? The one-tailed test provides more power to the defeatist effect; a hypothesis may be used whenever you have a hypothesis about the direction of the effect. Before doing the test, remember all the consequences of missing an effect in the other direction. For example, you developed a new drug, and you think it can create a significant impact on the lives of diabetic people. You chose to maximise the ability by opting for a one-tailed test. After doing all the tests, you failed this, and the possibility is that it is not even an effective drug. The consequences of this are extreme. So, when to use one-tailed hypotheses? When you consider your research directions and conclude that they are unegligible and in no way irresponsible or unethical, then you can use this method.

One-Tailed Test Example 

Suppose you test a medicine which will help the patient recover faster from surgery. The drug will help the patient improve the recovery time and make it much faster. The hypothesis of your drug is that it will decrease the recovery time. As we talked in the upper section, the hypothesis includes the null and the alternative hypotheses.

• Null hypothesis: The drug is new and does not improve the recovery time or make it worse.
• Alternative hypothesis: The drug will help the patient to recover faster, as it improves the recovery time.

In these types of cases, we use the one-tailed test, as you are only interested in testing if the drug will work or not. If the drug does work and decreases the recovery time, then you can avoid the null hypothesis and conclude the drug works. These types of tests can be used in different subjects, like you can use it in accounting get results, on the other hand if you face any problem in your work, than you can seek accounting assignment help form experts.

What is Two-Tailed Hypothesis? 

The two-tailed test is also known as the non-directional test, and because of this, you can do the test for the effect in both directions. Whenever you try to perform a two-tailed test, you have to split the significance level percentage between both tails of the distribution.

In the two-tailed test, the generic rule is that the effect equals zero, and the alternative is that the effect does not equal zero. All these hypotheses depend on the test you are trying to perform.

So, after defining the two-tailed hypothesis, you must be clear about the differences between these two and how they have different roles in statistics. Like statistics, the two-tailed method can be used in economics to get better outcomes. On the other hand, if you have any problems with your work, you can seek economic assignment help from experts. The expert will provide you with a well-researched and proofread project.

Use of Two-Tailed Hypothesis

The question is, when is a two-tailed test used? The two-tailed test is done when the sample you are testing is significantly different from the other and your interest is in the change in the direction, whether it is lower or greater. This type of test comes in handy when you're unsure about the results and want to know the results in both directions. This test is used in research when you examine a new drug with the presumption of its success or failure.

Two Tailed Test Example

For instance, you work for a company that makes light bulbs. The test is, you want to know the lifespan of a bulb. How long does it survive? In the research, you're not sure if it will survive long or short.

• Null hypothesis: The average life of the bulb is 1000 hours.
• Alternative hypothesis: The life span is not equal to 1000 hours.
• You have to do the test for 100 hours to know the lifespan of the bulb. After that, you will get the decision.
• If the test falls in one of the tails, then you have to reject the null hypothesis, which means the bulb lifespan is not long until 100 hours. If it doesn't fall in the tail, then you have to reject the alternative hypothesis.

So, this was the example of the two-tailed t-test after all the explanations. I hope you understand these types of hypotheses now, and you know how to perform these in your future project. As we move forward in the blog now, it is to address the real and main topic of the blog, one-tailed vs two-tailed hypotheses, so let's talk about the differences between them and how these are different from each other.

Comparing One-Tailed and Two-Tailed Hypothesis Tests

In the last section, we talked about the one tailed and two tailed tests, when you can use them, and how you can use them practically with examples. In this section of the blog, we will talk about the one tailed test vs two tailed hypothesis test. To know about the difference, read the lower table.

One Tailed vs Two Tailed Examples

ONE-TAILED GRAPH and TWO-TAILED GRAPH

Scenario: An organization has introduced a new marketing strategy and wants to find out its effect on their sales.

Facts and Figures: 

These are the figures for their sales, and from these, we have to find out the effect of the new marketing strategy.

Hypothesis Testing:

The Null Hypothesis is the same for both one tailed and two tailed hypothesis testing. The Alternative Hypothesis, however, is different. For One-tailed, it implies an increase, whereas for two-tailed, it implies either an increase or a decrease.

Significance level = 0.05

Solution:

To test for one-tailed and two-tailed hypotheses, we have to:

1. Find the means and standard deviations for both before and after the implementation of the marketing strategy data.

Mean for Sales Before Strategy =205.5
Mean for Sales After Strategy = 233.5
Standard Deviation for Sales Before Strategy = 14.27
Standard Deviation for Sales After Strategy = 14.22

2. Calculate the standard error of the difference of the two means.

Standard Error = 6.38

3. Find out the t-statistic.

t-statistic = 4.37

4. Determine the degrees of freedom

Degrees of Freedom = = 18

5. Find the critical t-value (from a t-distribution table, or using the calculator).

One-Tailed = 1.734
Two-Tailed = 2.101

6. Compare and make a decision.

For the one-tailed hypothesis, since the t-statistic is greater than the calculated t-value, the null hypothesis was rejected and that means that the strategy increased the sales.

For two-tailed hypothesis, the same logic follows. The null hypothesis is rejected, and the strategy has an effect on sales. Furthermore, since the mean of the sales after the strategy is greater than the sales before the strategy, that implies that the marketing strategy had a positive impact on sales.

After reading this blog, we hope you get the answer to what a one tailed vs two tailed test hypothesis is.In the blog, we discussed the two different tailed hypotheses, which are helpful for students of various fields like economics, accounting, psychology and more. These tales are different from each other, as you have read in the blog, but if you still face problems in using them in the right way or any other problem, you can seek assignment help UK from experts. The experts will provide a better and properly calculated assignment without any mistakes or errors, so seek our help and get a perfect assignment to score well academically.