While you may have heard about machine learning and statistical learning, the two are not similar. The only notable similarity between machine learning and statistical learning is that both are data-driven. Data is a valuable asset and important for maximizing any business. A good statistician will always have a firm grip on the meaning of statistical methods.

Understanding the differences between parameters and statistics will help you get the most from your data.

The significant difference between the two types of learning that we will explore is that parameter learning uses regression while statistical learning uses classification.

This section will discuss the two main types of data-driven learning: parameter and statistic. As seen below, they are both used to learn specific parameters of a machine learning algorithm.

**What is the meaning of Parameter?**

A parameter is an important aspect of statistical analysis. It relates to the characteristics that distinguish a particular population, and a parameter denotes a characteristic shared by the whole population.

When establishing inferences about the population, the parameters are unknown since collecting data from each individual would be impractical. Instead, we get a section from a sample statistic drawn from the population: The mean (average) height of all persons in the United States is 70 inches.

The mean (average) height of 10 randomly selected persons in the United States is 68 inches. Bear in mind that the mean of 68 inches differs from the mean of 70 inches and that the results will vary depending on whether you compute the mean for 20 individuals, 100 people, or 1,000 people.

The parameters have single values, whereas statistics provide many distinct results for each calculation. This is due to the lack of set values in statistics. For instance, a parameter describes the average amount of loans issued to Xyz University students.

This sample mean allows the researcher to draw conclusions about the population parameter, which deals with random samples rather than populations.

**Parameter vs. Statistic**

A parameter describes a whole population based on all its members, and a statistic describes a sample.

For example,50% of manufacturing workers like beef burgers for lunch, and that’s a criterion. You can’t ask all the guys if they prefer beef burgers for lunch, so it’s difficult to know. In such an instance, you’d definitely poll a representative sample and extrapolate to the entire male population. This gets us to statistics.

It’s a measure of a fraction (a sample) of the studied population. A sample is a segment of a population, and using sample data lets you estimate population characteristics.

**The Differences Between a Statistic and a Parameter **

A parameter is a constant measurement that characterizes the entire population. In contrast, a statistic is a feature of a sample subset of the target population.

A constant number with an unknowable value is referred to as a parameter. A statistic, on the other hand, is a number that is known and whose value changes based on the proportion of the population. Sample statistics and population parameters can be written in different ways in statistics:

As an example, In the parameter for the population, the population proportion (P), the mean is (Greek letter mu). 2 is the symbol for variance, N is the size of the population, and (Greek letter sigma) is the symbol for the standard deviation.

The standard error of the mean is shown by (x), the coefficient of variation by (/), the standardized variate by (X-/), and the standard error of proportion by (p).

In sample statistics, the mean is shown by x (x-bar), and the sample proportion is shown by p. (p-hat). (s) stands for the standard deviation, and (s2) stands for the variance. (n) is the size of the sample, and (sx) is the standard error of the mean. The standard error of a proportion is written as (sp), and the coefficient of variation is written as (s/(x)). (x-x)/s stands for standard deviation (z).

**Most Common Parameters**

The median, mode, and mean are some of the most common measures of central tendency. The following are the most common parameters:

**1. Mean**

The mean, also known as the average, is the most popular of the three methods for determining central tendency. Researchers frequently make use of the term “mean” when they are having a conversation about the distribution of ratios or gaps in the data. It is also the most well-known. You can determine the mean by adding up all the values and dividing them by the total number of points.

**2. Median**

The median is used to figure out the values of variables on ordinal, interval, or ratio scales. The median is calculated by ranking the numbers from least to most, then choosing the middle number or numbers. The median is typically the value in the middle when there are an even number of data points. If the numbers are even, we get the median by adding the two values in the middle and dividing by two to get the average.

**3. Mode**

The mode is the most common number in a group of numbers. It reveals the most frequent digit or value across the entire dataset. The mode is used for all types of data.

**Conclusion**

Parameters and statistical data are both relative but different ways of measuring a concept. The first focuses on the whole group, while the second deals with subsets.