Final answer:
Qualitative models are used when historical data are lacking or irrelevant, relying on expertise and opinion rather than precise data correlation. Statistical models, which involve probability, require substantial historical data for accurate predictions and are used in fields like meteorology and finance.
Step-by-step explanation:
When historical data are scarce, not available, or irrelevant, qualitative models are used and are based on intuition or an informed opinion. These models are used in scenarios where the precise mathematical correlation between data points cannot be established or where data may not even exist. Instead of statistical calculations, qualitative models rely on expert opinions, case studies, and the analysis of subjective factors. For instance, qualitative forecasting in business might include methods such as the Delphi technique, scenario building, and market research. Statistical models, on the other hand, are more appropriate when there is a substantial amount of relevant historical data that can be analyzed mathematically to predict future trends or outcomes. Statistical models often incorporate concepts such as probability to estimate the likelihood of certain events, which can be extremely helpful in fields such as meteorology or finance.