Hypothesis Testing: A Visual Introduction to Statistical Significance

What is Hypothesis Testing?

Hypothesis testing is a statistical process of determining whether a hypothesis is supported by the available evidence. A hypothesis is a statement about the relationship between two or more variables. The null hypothesis is a statement that there is no relationship between the variables, while the alternative hypothesis is a statement that there is a relationship between the variables.

Hypothesis Testing: A Visual Introduction To Statistical Significance

by Scott Hartshorn

4.5 out of 5

Language	:	English
File size	:	7521 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
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The significance level is the probability of rejecting the null hypothesis when it is actually true. The p-value is the probability of observing the data that was observed, assuming that the null hypothesis is true.

If the p-value is less than the significance level, then the null hypothesis is rejected and the alternative hypothesis is accepted. Otherwise, the null hypothesis is not rejected.

Why is Hypothesis Testing Important?

Hypothesis testing is an important tool for researchers because it allows them to make inferences about the population from a sample. By testing hypotheses, researchers can determine whether their results are statistically significant, meaning that they are unlikely to have occurred by chance.

Hypothesis testing is also used to make decisions about the design of experiments. By testing different hypotheses, researchers can determine which variables are most important to study and which methods are most likely to produce meaningful results.

How to Conduct a Hypothesis Test

Conducting a hypothesis test involves the following steps:

1. State the null hypothesis and alternative hypothesis. 2. Collect data. 3. Calculate the test statistic. 4. Determine the p-value. 5. Make a decision.

The test statistic is a measure of the difference between the observed data and the data that would be expected under the null hypothesis. The p-value is the probability of observing the data that was observed, assuming that the null hypothesis is true.

The decision of whether to reject the null hypothesis is based on the p-value. If the p-value is less than the significance level, then the null hypothesis is rejected and the alternative hypothesis is accepted. Otherwise, the null hypothesis is not rejected.

Hypothesis testing is a powerful tool for researchers. By testing hypotheses, researchers can make inferences about the population from a sample and make decisions about the design of experiments.

Hypothesis Testing: A Visual to Statistical Significance provides a clear and concise explanation of the basic concepts of hypothesis testing. This book is an essential resource for researchers who want to use hypothesis testing to make informed decisions about their data.

Hypothesis Testing: A Visual Introduction To Statistical Significance

by Scott Hartshorn

4.5 out of 5

Language	:	English
File size	:	7521 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	108 pages
Lending	:	Enabled

FREE