Statistical Significance
Statistical significance is when a result from a test or survey is too strong to be caused by luck alone.
Imagine flipping a coin and getting heads five times in a row. That could be luck. But if you flip it 100 times and get heads 90 times, something real is going on. The coin is probably not fair.
Explaining statistical significance by grade level
Flip a coin ten times. You expect about five heads and five tails. But what if you got nine heads? That feels too odd to be just luck. When a result seems too big to happen by chance, it matters.
Projects that explore statistical significance
Statistical significance tells you whether a pattern is too strong to blame on chance. Here, you build artificial seasons based on chance alone and compare them to real baseball streak counts. If real streaks differ from the random models at both 95% and 99% confidence levels, the result is statistically significant — meaning something beyond luck is shaping how teams win and lose in a row.
A survey result is statistically significant when the finding is too strong to blame on luck. This project tests that idea directly. You measure left-handedness across groups of 100, 200, 300, 400, and 500 participants. As group size grows, results become more accurate — and it gets easier to tell whether a pattern reflects something real or just lucky sampling.