Final answer:
A high degree of correlation in time-series data that does not imply causation is called a spurious correlation. It occurs when variables are correlated by chance or due to a third factor, and significance is measured by a correlation coefficient substantially different from zero.
Step-by-step explanation:
When analyzing time-series data, finding a high degree of correlation typically means there is a strong relationship between two variables; as one variable changes, the other tends to change in a predictable way. However, this high degree of correlation does not always imply causation. The situation where two variables have a high correlation but are not causally related is known as a spurious correlation. For example, two variables might both be increasing over time due to coincidence or because they are both influenced by a third, unobserved factor.
It is crucial to consider the possibility of confounding factors that may underlie the apparent relationship, and rigorous statistical analysis is required to discern causation from mere association. Correlation is measured by the correlation coefficient, a statistic that ranges from -1 to +1, indicating the strength and direction of the relationship between variables. This coefficient is significant if it is substantially different from zero, suggesting a noteworthy linear relationship between the variables.