The study looked at how the Granger spurious causality test works with small amounts of data. They found that the chance of finding a causal relationship goes up when the data is more consistent over time, but goes down with smaller sample sizes. Changing the test method didn't make a big difference. The type of data and errors in the data play a big role in causing false causal relationships. This research helps us understand how to avoid mistakenly thinking one thing causes another.