But is the difference scientifically important? The confidence interval helps you decide. The difference is statistically significant. You can reject the idea that the difference is a coincidence, and conclude instead that the population has a mean different than the hypothetical value you entered. If the P value is small (usually defined to mean less than 0.05), then it is unlikely that the discrepancy you observed between sample mean and hypothetical mean is due to a coincidence arising from random sampling. You just don't have evidence of a difference.
This is not the same as saying that the true mean equals the hypothetical value. If the P value is large, the data do not give you any reason to conclude that the population mean differs from the hypothetical value you entered. If the data were sampled from a Gaussian population with a mean equal to the hypothetical value you entered, what is the chance of randomly selecting N data points and finding a mean as far (or further) from the hypothetical value as observed here? A one-sample t test compares the mean of a single column of numbers against a hypothetical mean that you provide.
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