R² may be interpreted as less than α of data variability that can be accounted for by the model. Hence the correct option is c.
The coefficient of determination, denoted as R², is a statistical measure that represents the proportion of data variability that can be explained or accounted for by a statistical model. Specifically, it indicates the percentage of the dependent variable's variance that is captured by the independent variables in the model. R² ranges between 0 and 1, where 0 implies that the model explains none of the variability, and 1 implies that the model explains all the variability.
In hypothesis testing, particularly when testing the null hypothesis (H₀), the p-value plays a crucial role. The p-value represents the probability of observing a test statistic as extreme as, or more extreme than, the one observed, under the assumption that the null hypothesis is true. A common threshold for significance is denoted by α, typically set at 0.05.
To reject the null hypothesis (H₀), one looks at the p-value:
If the p-value is less than α, it provides evidence to reject H₀, suggesting that the observed results are unlikely under the assumption of the null hypothesis.
If the p-value is greater than α, it indicates that the observed results are likely to occur by chance, and there is insufficient evidence to reject H₀.
If the p-value is exactly equal to 1, it suggests that the observed results are highly likely to occur under the null hypothesis, and there is no evidence to reject it.
Therefore, the correct statement is that another means of rejecting H₀ is if the p-value is less than α (option c). This criterion helps researchers make informed decisions about the significance of their findings based on the observed data and the chosen level of significance. Hence the correct option is c.