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A statistical hypothesis testing is analyzing a supposition or proposed explanation made as a starting point for further investigation based on limited evidence. On the other side, statistics is a study or science of collecting and analyzing vast quantities of numerical data, particularly to infer proportions overall from the portions in a representative sample.

The two concepts are merged to form a standing block in solving statistical data. In this article, you are going to understand what statistical hypothesis entails. You can as well seek for statistics homework answers and more clarity on the subject today by getting in touch with us.


Statistical testing of hypothesis, also known as confirmatory data analysis, is an analytical method used by experimental data to make statistical decisions. It's an assumption you make about the parameter of a population. This means that it evaluates two statements about a population that are mutually exclusive to determine which statement is best supported by sample data.

The theme tends to act as a rule that specifies whether to accept or reject a claim about a population depending on the evidence provided by a sample of data. Also, the statistical hypothesis testing examines two opposing hypotheses, that is the null hypothesis and alternative hypothesis.

Types of Statistical Hypothesis Testing

Null hypothesis

This type of hypothesis states that the exact opposite of what an investigator or experimenter predicts or expects to happen. This describes the statement which asserts that there is no positive relationship between the variables.

The Alternative Hypothesis

An alternative hypothesis makes a statement that suggests or advises a potential outcome or result that a researcher may expect. It is broadly divided into two types of alternative hypothesis.

Directional alternative hypothesis

It is also known as the directional hypothesis. This type of alternative hypothesis explains the direction of the expected findings. It is developed to examine the relationships among the variables rather than the comparison between the groups.

This prediction is based on past research, accepted theory, extensive experience, or topic literature. Typically, a researcher develops a directional hypothesis from research questions and uses statistical methods to check the hypothesis ' validity.

Non-directional alternative hypothesis

Well known as the two-sided hypothesis is a kind of alternative hypothesis that has no definite direction of the expected findings being specified; instead, it only explains that a difference exists.

Significance of Statistical Hypothesis Testing

Both the null and alternative hypothesis contributes to the importance of the subject. First and foremost, they provide an approximate description of the phenomena and offer the researcher a relational statement that is directly tested in a research study. They act as a purpose to provide a framework for reporting the inferences of the study.

In addition, they take up the role of working instruments of the theory. To top it up, statistical hypothesis testing serves the purpose of proving whether or not the test is supported, which is separate from the investigator's values and decisions. Last but not least, the subject matter provides direction to the research.

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