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Null Hypothesis Definition: The null hypothesis is a fundamental concept in statistical analysis. It is used to make decisions based on data and statistical tests. The null hypothesis is represented by the symbol of H0. In this article, you will understand the Null Hypothesis in detail.
Null Hypothesis Meaning
The null hypothesis means that there is no significant difference or effect between two samples or variables. It serves as the default position that any observed differences are due to chance rather than a specific cause. The null hypothesis helps in making objective decisions by providing a baseline to test against.
According to the null hypothesis, there is no significant correlation between an independent variable and a dependent variable when considering all samples. This means that changes in one variable are not expected to affect the other variable in a significant way. Statistical tests are used to determine whether there is sufficient evidence to reject the null hypothesis in favour of an alternative hypothesis.
Representation and Testing of the Null Hypothesis
- If the experimental outcome matches the theoretical outcome predicted by the null hypothesis, then H0 is considered to hold true. This means that the observed data is consistent with the assumption of no effect or difference.
- If there are significant differences in the observed parameters across the samples, the null hypothesis is rejected. This indicates that the observed data deviates from what would be expected under the null hypothesis, suggesting that the alternative hypothesis (H1 or Ha) may be true.
- Rejecting the null hypothesis does not imply that there were errors in the experimental design. Rather, it means that the data provides sufficient evidence to support a significant effect or difference. This sets the stage for further research and exploration.
Null Hypothesis vs. Alternative Hypothesis
Below is the definition and difference between the Null Hypothesis and the Alternative Hypothesis.
- Null Hypothesis (H0): Null Hypothesis states that there is no significant difference or effect between two given samples or variables. It serves as the default assumption.
- Alternative Hypothesis (H1 or Ha): Alternative Hypothesis states that there is a significant difference or effect between two given samples or variables. It is the opposite of the null hypothesis and is considered when there is evidence to reject H₀.
The null hypothesis may be rejected due to experimental or sampling errors that reveal significant differences or relationships between variables.
Test for Null Hypothesis
Testing the null hypothesis involves various statistical methods to determine whether there is enough evidence to reject it. Two of them are significance testing and hypothesis testing.
Significance Testing: To assess whether the observed data provides strong enough evidence to reject the null hypothesis in favour of the alternative hypothesis. The purpose of such testing is to assess how much weight can be put behind statements made against this particular theory. There are four important steps in significance testing:
- First outline what you believe about both your null and alternative hypotheses.
- Compute test statistics.
- Obtain p-values for those tests.
- Compare p-value with α (Significance Level)
- Reject H0: If the p-value is less than the significance level (α), reject the null hypothesis. This indicates strong evidence against H₀.
- Accept H0: If the p-value is greater than α, do not reject the null hypothesis. This suggests insufficient evidence to support H₁.
Hypothesis testing: Hypothesis testing is used to derive conclusions about a population based on sample data and make informed decisions about hypotheses. A hypothesis is an educated guess about a certain sample that can be tested either through experiment or observation. Initially, a tentative assumption is made about this specific sample in the form of a null hypothesis.
There exist four steps for carrying out hypothesis testing. They are:
- Identify the null hypothesis.
- Define the null hypothesis statement.
- Choose the test to be performed.
- Accept the null hypothesis or the alternate hypothesis.
Hypothesis testing comes with its own share of errors. Below are two major categories of error in hypothesis testing.
Type I Error:
Type I Error includes rejecting the null hypothesis when it is actually true. This error suggests that a significant effect or difference was detected when there was none.
Type II Error:
Type II Error includes accepting the null hypothesis when it is actually false. This error indicates that no significant effect or difference was detected when there actually was one.
Hypothesis testing allows statistical inferences from population data. Through testing of assumptions the analysis tool indicates how likely something is within a specified level of precision. This method is used to ascertain whether experimental outcomes are legitimate.
Before conducting hypothesis test, null hypothesis and alternative hypothesis are established. In understanding samples drawn from a larger population, this assists in arriving at conclusions. More information on what is hypothesis testing, its types, steps in carrying out such tests with examples will be contained in this paper.
Hypothesis testing formula
There are various types of hypothesis testing which are used to determine if the null hypothesis can be rejected or not depending on the kind and size of available data. Below are some important test statistics with their respective hypothesis testing formulas:
Why is Null Hypothesis Important?
- Null hypothesis is important because it is the basis for statistical testing. It is the hypothesis that is tested against the alternative hypothesis to see if the data supports the alternative hypothesis.
- Null hypothesis is important because it is the basis of statistical inference. It is the hypothesis that is tested for statistical significance. If the null hypothesis is rejected, then it is concluded that the alternative hypothesis is true.
Null Hypothesis Symbol
- Null hypothesis is a statement that the researcher tries to disprove. It states that there is no difference between the groups or that the population is not evenly split. The null hypothesis is symbolized with the letter H0.
- Null hypothesis symbol is a mathematical symbol used in statistics to denote the hypothesis that there is no difference between the means of two groups. The symbol is usually written as H0. The null hypothesis is usually contrasted with the alternative hypothesis, which is the hypothesis that there is a difference between the means of the two groups.
Example
The null hypothesis is a statement that is tested against an alternative hypothesis to determine the statistical significance of a result. The null hypothesis is that there is no difference between the groups being studied. The alternative hypothesis is that there is a difference between the groups being studied. The null hypothesis is usually the hypothesis that is tested when the researcher is not sure what the difference between the groups is.
Null hypothesis FAQs
What is a Null Hypothesis?
The null hypothesis (H0) is a statement used in statistical tests that assumes no significant difference or effect exists between groups or variables being studied.
How do you reject a Null Hypothesis?
A null hypothesis is rejected when the p-value obtained from a statistical test is less than the predetermined significance level (α), typically 0.05. This indicates that the observed data provides strong evidence against H0.
What is the difference between Null and Alternative Hypothesis?
The null hypothesis (H0) assumes no effect or difference, while the alternative hypothesis (H1) suggests that there is a significant effect or difference. Rejecting H0 implies accepting H1, indicating that the observed results are statistically significant.
How do you write a symbolic null hypothesis?
A symbolic null hypothesis is written using statistical symbols to represent the relationship being tested. Typically, it is denoted as H0, followed by a statement that indicates no effect or no difference between variables.
