Importance Of Hypothesis Testing In Inferential Statistics

In social sciences, inferential and descriptive statistics are two standard methods to organise and extract conclusions from the dataset. The descriptive statistics summarise the characteristics of the dataset. Moreover, inferential statistics logically concludes your finding and help in predicting future trends after observing patterns in the dataset. It needs you to collect a sample and understand the larger population from which the sample is taken. Hypothesis testing is the process of testing a claim or educated guess about the possible outcomes of research. This article aims to discuss the significance of hypothesis testing in inferential statistics after revising a few very basic concepts related to it.

Inferential Statistics- A Brief Introduction:

As a matter of course, inferential statistics allow researchers to extract conclusions and make predictions. The majority of the time, for solving a scientific problem based on facts it is too expensive and difficult to collect data from a large population. For inferential statistics, you need to randomly collect samples from a population. If your sample is not the best representative of the whole population, inferential statistics cannot extract valid inferences. Inferential statistics has two main uses, estimating parameters and hypothesis testing.

1.1 Estimating Parameters:

It is the process of taking statistics from sample data and analysing it to say something about a pre-defined population parameter. For example, if you collect a sample randomly from adult women in the United States and the aim of your study is to measure their heights, weights, and age, then it will be the parameters’ estimation in statistics. If you have any confusion in getting the estimate values, you can hire best assignment writing services.

1.2 Testing Hypothesis:

It is the method of research where you answer research questions. Usually, in this method, a researcher forms a hypothesis and applies different statistical methods to test the trustfulness of your educated guess. For example, let us consider that our hypothesis for research is ‘Honey has phytochemicals that are anti-cancer in nature’. The inferential statistics allows you to collect data to see the anti-cancer properties of honey on a randomly selected animal model. Once you record data, then use statistical tests such as ANOVA, T-test, Z-score, and many more. Visual graphics are great tools for understanding trends or patterns in the recorded data. This is how inferential statistics allows us to extract logical conclusions by testing the relationship between the dependent and independent variables.

Importance of hypothesis testing in inferential statistics:

A hypothesis is a preposition or theory that is formulated as an explanation of the occurrence of a phenomenon. It guides researchers to select a suitable method of investigation during research. A scientific hypothesis can become a theory as well as a law if supported by a series of experiments.

2.1 Hypothesis Testing- Test of significance:

A Statistics Department at the San Jose State University declared hypothesis testing as the most important step in scientific research. It helps researchers to know whether a process really happens. It assists medical researchers in knowing whether a drug is really effective. The relational type of hypothesis tells researchers how manipulating one variable affects others. Simply, it gives an opportunity for the researcher to prove a claim based on statistical significance. All in all, the hypothesis testing is actually a test of significance.

2.2 Support your conclusion:

Hypothesis testing helps researchers to justify conclusion even in the case when no existing theory go in favour of your claim. Hence, in the following conditions you can take help from the hypothesis testing to validate your points:

• A business company can test the hypothesis by inferential statistics to claim the durability of their product. For example, if a battery manufacturer wants to prove a claim that XYZ battery would last more than 80 hours on a single battery-oriented light then the hypothesis testing can help them to make their claim valid despite no existing supporting theory in a company’s favour.

• On the outbreak of a pandemic, to prove the efficacy of a newly formed vaccine, hypothesis testing as a part of inferential statistics may prove useful.

• For a newly launched brand, the effectiveness of brand awareness on the revenue generation problem can be solved by testing the hypothesis.

• Hypothesis testing best works for exploring the relationship between the two or more categorical features in research. The categorical data includes variables that belong to different groups such as gender, size, educational level, and age groups.

• Hypothesis testing is also important to explore the relationship between categorical and continuous data by using ANOVA.

2.3. Additional points to specify the importance of hypothesis testing in inferential statistics:

The most important point that specifies the significance of hypothesis testing is it allows even a novice researcher to evaluate the strength of his own claim or assumption. In addition to this, other benefits of hypothesis testing in inferential statistics include:

• It helps researchers to follow a reliable framework for making research decisions more easily.

• It allows researchers to link the specific research questions and underlying theory.

• It is one of the most important processes to measure the reliability and validity of outcomes.

• Hypothesis testing in inferential statistics facilitates researchers in knowing whether the data is significantly important or not.

• It helps in accessing the probability of research progress and failure.

• It is the way to describe the research study in concrete terms instead of theoretical basis.

Final Thoughts:

Inferential statistics is one of the pillars of statistical analysis. It is important to extract the conclusion from a large data set. Both parameters’ estimating and hypothesis testing help us in revealing a number of hidden facts in the data. However, hypothesis testing is important due to a number of reasons. It helps in validating your claim, estimating statistical significance, accessing chances of progress, and proving a point without having existing theories. Its major role in the scientific process is to make predictions and check whether a thing really happens. All in all, to better proceed in the scientific process, hypothesis testing refers to the shortest possible path to solve scientific problems based on assumptions.

Author Bio:

Robert Fawl is a professional Content Writer based in London, UK. He is an author and blogger with experience in writing different academic assignments. If you need dissertation writing services to write your dissertation, you can contact him.