The Central Limit Theorem is a fundamental theorem in statistics that states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger, no matter what the shape of the population distribution. This outcome is significant because it enables us to make statistical inferences about the population mean based on the distribution of sample means.

Ridge regression handles multicollinearity by introducing a degree of bias to the regression estimates, reducing their variance, and making them more reliable.

The Spearman’s Rank Correlation test assumes that the variables are ordinal or continuous and that the relationship between them is monotonic. It does not require the relationship to be linear or the data to be normally distributed.

The main limitation of the mean as a measure of central tendency is that it is highly sensitive to outliers or extreme values. An outlier can skew the mean and make it a less accurate representation of the data. Moreover, mean does not describe the middle value or most common value in the dataset, which are often important characteristics.

A 95% confidence interval estimates the range within which we are 95% confident that the true population parameter lies. It is not about the range of the data or the mean of the sample.

In a Chi-square test for independence, small expected frequencies can lead to a larger Chi-square value. This is because the Chi-square value is inflated by small expected frequencies, which can lead to a significant result even when there is no substantial relationship between the variables.

Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. It's an inferential statistic that allows us to infer if our observed results deviate from null hypothesis by chance or by a true statistical difference.

In the context of hypothesis testing, the p-value is the probability of observing a test statistic as extreme as the one calculated, assuming that the null hypothesis is true.

The key assumptions for a Poisson distribution are that the events are happening at a constant mean rate and independently of the time since the last event. This is often used for modeling the number of times an event occurs in a given interval of time or space.

In a normal distribution, the mean is the center of the distribution and represents the "average" value. The standard deviation measures the dispersion around the mean. Roughly 68% of the data falls within one standard deviation of the mean in a normal distribution.