Alternative Hypothesis

What is Hypothesis?

Hypothesis is a working statement or a theory that is based on insufficient evidence. As a result, it gives way to further testing and experimentations. The outcome of the experiment could either be true or false. The two groups being compared are the independent variable. Before testing a hypothesis, one thing should be clear in our head, “Is there something causing something else?” if you think the answer is yes then think about “what is causing what”. Let's take an example to understand this better. Teachers who are given higher pay are expected to have positive attitudes towards the children while the teachers with lower pay are expected to have poor attitudes towards the children. The first thing that needs to be questioned is “are two or more groups being compared?”. If the answer is a yes then we come to the next question i.e., what groups are being compared? It is between teachers who get higher pay vs teachers who get lower pay. Thus the independent variable is teachers pay while the dependent variable is attitudes towards children.

Types of Hypothesis

There are two types of statistical hypothesis.

  1. Null Hypothesis: A null hypothesis can be defined as a hypothesis where there is no statistical significance between the two variables in the hypothesis. The researcher tries to disprove the hypothesis. It is denoted by H0.

  2. Alternative Hypothesis: An alternative hypothesis, on the other hand, states that there is statistical significance between two variables. It is denoted by H1 or Ha.

Let’s take another example, you desire to see whether a coin will be fair and balanced. A null hypothesis might support that half of the total flips would result in Heads and half in Tails. The alternative hypothesis, on the other hand, might support that the number of Heads and Tails would be very different. Symbolically, these hypotheses would be expressed as:

H0: p = 0.5

Ha: p <> 0.5

Suppose the coin was flipped 50 times, resulting in 40 Heads and 10 Tails. Given this result, we would be inclined to reject the null hypothesis as the coin was probably not fair and balanced.

Alternative Hypothesis

The alternative hypothesis can be defined as one in which a difference between two or more variables is anticipated by the researchers. In other words, the observed pattern of the data is not due to a chance occurrence. The concept of the alternative hypothesis is a central part of formal hypothesis testing.

For example, in the development of medicine, a hypothesis can be formulated that a new treatment for a disease is better than the existing one. Therefore, this hypothesis would be called an alternative hypothesis. But if the new treatment fails to be proved correct, it will be called as a null hypothesis. To summarize, a null hypothesis is further examined to see if it's true or false. If the null hypothesis stands rejected, the alternative hypothesis will be accepted.

Basically, the alternative hypothesis and null hypothesis are just opposite to each other. If your null hypothesis is “ there is more water in glass A than B'' then your alternative hypothesis will be “there is more water in glass B than A”. The alternative hypothesis is usually a statement that a researcher thinks is true which ultimately leads you to reject the null hypothesis and replace it with the alternate hypothesis.

Examples of Alternative Hypothesis

Example 1: Ethanol’s boiling point is 173.1°F.


Solution 1:  There is a theory that states ethanol actually has a different boiling point, of over 174°F. The accepted fact that “ethanol boils at 173.1°F” is the null hypothesis while the theory “ethanol boils at temperatures of 174°F” is the alternate hypothesis.

Example 2: Elementary school students are performing at lower than average levels on standardized tests. 

Solution 2: It would be a general thought that low test scores are due to poor teacher performance. However, you have a theory that the reason behind students performing poorly is because their classroom is not as well ventilated as the other classrooms in the school. Therefore, the accepted theory that the “low test scores are due to poor teacher performance” will be the null hypothesis while the theory that states “low test scores are due to inadequate ventilation in the classroom” stands as the alternative hypothesis.

Difference Between The Null Hypothesis And Alternative Hypothesis

The difference between a null hypothesis and an alternative hypothesis is stated as follows:

  1. The null hypothesis is often denoted as H0 while the alternative hypothesis is normally denoted as H1.

  2.  A null hypothesis states the exact opposite of what an investigator predicts or expects whereas an alternative hypothesis makes a statement that suggests or advises a potential result or an outcome that an investigator may expect.

  3. A null hypothesis basically states that there is no exact or actual relationship between the variables. An alternative hypothesis contradicts it.

FAQ (Frequently Asked Questions)

Question 1: How is the Directional Alternative Hypothesis Different From the Non-Directional Alternative Hypothesis?

Solution 1:  Directional alternative hypothesis is a kind that explains the direction of the expected findings. It uniquely examines the relationship among the variables instead of comparing between the groups.

Nondirectional hypothesis, on the other hand, is a kind that has no definite direction of the expected findings. 

Question 2: What is the Difference Between a Hypothesis and a Theory?

Solution 2: the difference between a hypothesis and a theory can easily be drawn. A hypothesis is merely a potential explanation or guess. A theory is a tool used to explain an idea or a principle. A hypothesis is subjective while a theory is an objective. Theories originate from hypothesis after it is found to be valid. A hypothesis is then generalized and formulated into principles and equations which then is applied to solve problems. It thus takes the name of theories. The distinction between theories and hypothesis was once (or maybe still) confusing because in everyday language they used to mean the same. It might be easy for a non-scientist to mute the distinction as they are not in the field of developing theories but for scientist who values the distinction between them will never use the word theory to denote their guesswork