Final Answer:
A hypothesis in statistics is a statement about a population parameter. In hypothesis testing, the null hypothesis (H0) posits no effect or difference, while the alternative hypothesis (H1) asserts the presence of an effect or difference. Statistical hypothesis testing evaluates the evidence from sample data to make inferences about the population parameter specified in the hypothesis.
d) A population parameter
Step-by-step explanation:
In statistics, a hypothesis serves as a formal statement that proposes a claim or assumption about a population parameter. The null hypothesis (H0) is a fundamental component in hypothesis testing, representing a statement of no effect or no difference in the population. It is a starting point that researchers aim to either accept or reject based on empirical evidence.
Contrastingly, the alternative hypothesis (H1) is formulated as a statement indicating the presence of an effect or difference. The objective of hypothesis testing is to assess the strength of evidence in the sample data against the null hypothesis. This process involves statistical analysis to determine whether the observed results are likely to have occurred by random chance or if they provide sufficient grounds to reject the null hypothesis in favor of the alternative.
Researchers use various statistical methods, such as t-tests or chi-square tests, to evaluate the likelihood of observing the obtained data under the assumption of the null hypothesis. The outcome of this analysis informs researchers about the statistical significance of their findings and guides them in making valid inferences about the population.
In summary, a hypothesis in statistics serves as a pivotal element in hypothesis testing, allowing researchers to systematically examine and draw conclusions about population parameters based on empirical data and statistical analysis.