Category: 2. Types of Hypothesis

This hypothesis states that there is a relationship between two variables but it does not predict the exact nature or direction of the relationship.

A directional hypothesis specifies the direction or nature of the relationship between two or more independent variables and two or more dependent variables. They are developed from research questions and use statistical methods for validation.

They are based on aspects such as:

1) Accepted theory

2) Past research

Associative hypotheses states that there is a relationship between two variables. It looks at how specific events co-occur.

Causal hypotheses state that any difference in the type or amount of one particular variable is going to directly affect the difference in the type or amount of the next variable in the equation. It looks at how manipulation affects events in the future.

The statement could be logical or illogical but if statistic verifies it, it will become a statistical hypothesis.

Vitamin C is good for skin. You would have to test this hypothesis on a group of people to verify it. This is a statistical method of verifying the statement.

As the name suggests, it is verified logically. The process of verification involves:

• Agreement
• Disagreement
• Difference of opinion.

Example:

Hypothesis statement: An animal can not survive without water.

Logical verification: This is true because all living beings need water.

It is also known as a maintained hypothesis or a research hypothesis

Firstly many hypotheses are proposed. Then among them, one is selected which is the most efficient.

There are four main types of alternative hypothesis:

Point alternative hypothesis: Population distribution in the hypothesis test is fully defined and has no unknown parameters.

Non-directional alternative hypothesis: It states that the null hypothesis is untrue.

One-tailed directional hypothesis: It is only concerned with the region of rejection for one tail of a sampling distribution.

Two-tailed directional hypothesis: It is concerned with both regions of rejection of the sampling distribution.

It is contrary to the empirical hypothesis, as it states that there is no relationship between dependent and independent variable. It essentially states that the data and variables being tested do not actually exist.

Example: Water does not affect the growth of a plant.

It is also called a ‘working hypothesis’ . It is an only an assumption during the formulation phase, but when it is tested it is no longer just an idea or notion. It’s actually going through some changes around those independent variables.

Example: Cotton clothes are better for summer than velvet clothes.

Complex hypothesis is that one in which there are multiple dependent as well as independent variables.

Example: Global warming causes icebergs to melt which in turn causes major changes in weather patterns.

The difference between a simple hypothesis and a complex hypothesis :

simple hypothesis: Relationship exists between two variables only.

complex hypothesis: Relationship exists between multiple variables.

It is also called a basic hypothesis. It shows the relationship between two variables where one is called the independent variable or ‘cause’ and other is called the dependent variable or ‘effect’.

Example: Global Warming causes icebergs to melt.

Here the cause is global warming and the effect is melting of icebergs.