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Understanding skewness in data distribution is crucial for interpreting real-world datasets. Skewness refers to the asymmetry in the distribution of data. When we say a distribution is skewed to the right (positive skewness), it means that the right tail (higher values) is longer than the left tail. This often happens when there are a few exceptionally large values dragging the mean to a higher number than the median.

In the context of job tenure, if most people work at a job for a shorter time but a few stay for much longer, this can pull the average (mean) upward. As a result, you might notice that the mean (6.2 years) is more than the median, suggesting right-skewed distribution. Recognizing skewness helps in making more informed decisions when analyzing data.

Normal distribution, often visualized as a bell curve, is a fundamental concept in statistics. It describes a data set where most occurrences take place close to the mean, and it symmetrically tapers off towards the extremes. A key feature of a normal distribution is that it is symmetrical and has a skewness of zero.

In the problem of estimating the average number of years 100 people work, the Central Limit Theorem assures us that even if individual tenure data isn't normally distributed, the sample mean of a large enough sample will be. This approximation forms a bell-shaped curve, regardless of the original distribution's shape. Thus, normal distribution provides a reliable method for understanding sample data behavior when sample sizes are large.

Sample size plays a pivotal role in statistical analysis and probability estimation. A larger sample size often leads to more reliable insights as it tends to represent the population more accurately.

According to the Central Limit Theorem, a sample size greater than 30 is usually considered sufficient to form a normal distribution of sample means. In the given exercise, a sample size of 100 adequately supports this, allowing for credible estimation that the average tenure for this group aligns closely with the expected normal distribution of population means.

Bigger samples help in smoothing out the variability found in smaller samples, providing clearer insights into the population's true characteristics and behaviors.

Probability estimation involves determining the likelihood of a particular event or outcome occurring. In statistics, this can relate to estimating the probability of an average or sum when considering sample sizes.

For 100 randomly selected individuals, estimating the probability that they worked an average of 10 years or more for the same employer becomes feasible due to the Central Limit Theorem. This method allows us to use the normal distribution of sample means for prediction since the sample size is sufficiently large. However, estimating the same probability for a single individual isn't viable using the CLT because individual job tenure likely doesn't follow a normal distribution and is more influenced by factors not averaged out.

Thus, accurate probability estimation depends significantly on sample size and the application of statistical theorems like the Central Limit Theorem.