What is the Difference Between Null and Alternative Hypotheses?
A hypothesis refers to the supposition or assumption generated at the beginning of a scientific process that the researcher is yet to prove on the basis of evidence and reasoning. Researchers conduct a hypothesis test using experiments and observations. Hypothesis testing is aimed at making inferences about a population parameter based on sample data obtained from that population.
Null and alternative hypotheses are two claims against or for which researchers weigh evidence by the use of a particular statistical test to answer a research question. The null hypothesis, denoted by H-zero (H0) implies that no effect or difference is expected on the population while the alternative hypothesis states that an effect/significant difference is expected or that a relationship exists between two variables.
The effect is measured in terms of how the independent variable affects the dependent variable. This article contains detailed information about the difference between the null and alternative hypotheses.
Differentiating the Null and Alternative Hypothesis
Generally, the null hypothesis denotes a statement where no relationship exists between two variables while the alternative hypothesis implies that some statistical significance between two measured phenomena exists. Researchers use statistical tests to determine which of the two hypotheses is supported by the evidence obtained from analyzing the collected data. Learn more about data analysis here.
One can reject the null hypothesis if there is enough evidence against the claim that no observed effect on the population when the p-value is equal to or less than the alpha value. The alternative hypothesis provides the answer to the research question. Some of the differences between the alternative and null hypotheses include:
1. The meaning of the null hypothesis versus the alternative hypothesis
The null hypothesis is a statement in which no significant differences or relationship exists between two selected variables while the alternative hypothesis implies that there is an effect on the population/there is sufficient evidence supporting the claim that there is an expected effect or difference between two groups/variables.
2. Mathematical symbols used to represent the hypotheses
The mathematical formulation of the null hypothesis is the equal sign while for the alternative hypothesis, the unequal sign is used. The null hypothesis uses the equality symbol including the equal sign, equal or less than, or equal or greater than symbols to explain the observed effect on the population.
Alternative hypothesis uses the inequality symbols such as the not equal to, less than, or greater than signs when describing the outcome or the research prediction.
3. When the either hypothesis is rejected or approved
Null and alternative hypotheses are mutually exclusive, hence, only one can be true at a given time. If the findings from the data collected from a random sample obtained from a target population provide enough evidence against the claim that there is no effect, the null hypothesis is rejected while the alternative hypothesis is approved/supported.
4. The logic of the hypothesis testing
Null hypothesis testing is based on a population parameter, hence, it is indirect and implicit while the alternative hypothesis is based on a sample statistic, hence, its testing is direct and explicit.
5. Implications of the approval or rejection of either hypothesis
If the null hypothesis is accepted, the researcher/scientist has to change the prior opinions or statements to match it. If the alternative hypothesis is accepted instead, no changes in the previous opinions and statements are required.
6. Labeling/representation of the hypotheses
The null hypothesis is represented by H-zero(H0) while the alternative hypothesis is represented by H1 or Ha.
7. Comparison between the p-value and the significance level
In the null hypothesis, the p-value is less than the significance level while in the alternative hypothesis, the p-value is greater than the level of significance.
8. Purpose of either hypothesis
The main purpose of the null hypothesis is to disapprove or verify a certain statistical hypothesis, advance theory, or confirm consistency in the results from multiple experiments. The alternative hypothesis is aimed at providing clarifications or restatements of research problems, directing a study to obtain the desired results, proving that a study is backed up with sufficient evidence rather than the researcher's values and opinions, and ascertaining the significance and relevance of research findings.
Frequently Asked Questions about Null and Alternative Hypotheses
Null hypothesis and alternative hypothesis are two mutually exclusive statements regarding a particular population. To perform a hypothesis test, the researcher uses sample data to base the decision on whether to reject the null hypothesis.
While the null hypothesis is a statement of no effect or no relationship between the independent and dependent variables, the alternative/alternate hypothesis states the research prediction of a relationship or significant difference between the variables. Some of the frequently asked questions about null and alternative hypotheses include:
a). Are alternative hypotheses one-sided or two-sided?
Alternative hypotheses can be either one-sided or two-sided. A one-sided/directional hypothesis is used when the researcher wants to determine whether the population parameter deviates from the hypothesized value in a particular direction.
A two-sided/non-directional hypothesis helps in determining whether the population parameter is less than or greater than the prior hypothesized value.
b). Is there a difference between a research hypothesis and a statistical hypothesis?
A statistical hypothesis represents the mathematical statement of a particular population parameter while the research hypothesis is the answer proposed for the research question and usually includes an explanation.
c). What is the significance of hypothesis testing in research?
When conducting data analysis for research, Hypothesis testing enables researchers to understand whether they can reliably generalize observed findings from sample data to the entire population from which the sample was drawn. The null and alternative hypothesis statements in a study must be exhaustive and not overlap.
d). What symbols represent null and alternative hypotheses?
The null hypothesis, abbreviated by H0 includes an equal to (=) sign when expressed in mathematical symbols. The alternative hypothesis (Ha or H1) includes an inequality symbol. The null hypothesis is accepted or rejected on the basis of evidence.
Before conducting a scientific process of inquiry, researchers or scientists ought to generate a clear and precise alternative hypothesis on which to base the study to generate reliable findings. The null hypothesis and alternative hypothesis statements must be properly and correctly defined before data collection and interpretation of research findings. Learn more about null hypotheses here.
