Statistics is the process of converting raw data to information that can be used by people. Apparently, it is difficult for people to make sense from a collection of numbers. Statistics combines different tools that make it easy for people to draw a meaning from a collection of data. The main function of these statistical tools is comparing a set of data to establish similarities, consistency in the data sets, and their respective characteristics.

**Big Idea in Statistics**

The one big idea to comprehend in statistics is referred to as the normal distribution. Normal curve or the bell curve are other names used to identify this big idea. The normal distribution shows the distribution of a group of numbers. Data such as age and height exhibit similarly distributed values where a few are small, many in the middle, and few are large values. Measurement is done from such a set of data and the distribution is analyzed graphically.

Data distribution describes how numbers in a set vary from low to high. In other words, data distribution is the frequency with which they occur in a set. Natural attributes such as height and age have the tendency of following a normal distribution in which the numbers in the middle are higher than the respective numbers on the extremes. Mean average is usually at the middle where the count is high.

**The Standard Deviation**

Standard deviation measures how much variance there is in a dataset. The variation in this case is measured by evaluating how far each number is from the mean average of the entire dataset. Standard deviation gives an idea on whether the mean average is a representative of all the numbers in the dataset.

A high standard deviation value means that there are many numbers are higher and/or smaller than the mean average. At this point, the mean average will not be useful in predicting a specific value. A small standard deviation translates to mean that all the values are closely related, and the mean average is close to any given value.

**Descriptive Statistics**

Descriptive statistics is the type of statistics that describes a set of data in a declarative approach and provides a summary view that that reveals more information than just looking at the data directly. Descriptive statistics describes how values are distributed in the data set, their tendency to concentrate around the middle value which is referred to as central tendency, and how the values disperse from the mean average.

**Central Tendency**

Central tendency defines the mean, the mode, and the median values. Through the measure of central tendency, statistics forms a group of values and picks a single value to represent the group. The mean measures the average of all the values in the set. The mode is the value that is repeated more than every other value. The median value is the value that is at the center of the dataset. Central tendency offers a clue about a dataset. When the mean and the median are close to each other in value, then there is a normal distribution in the dataset.

**Dispersion**

Dispersion defines both the standard deviation and the variance. Standard deviation is the variation in the datasets. Variation gives how much any given value in the datasets vary from the mean average. Values may be concentrated around a certain point, which gives an impression that something informed the grouping of these values. On the other hand, the values may be evenly distributed along the normal distribution curve giving an impression that the data is a representation of a natural phenomenon, or be unpredictable, implying no underlying factors facilitated the values.

**Correlation**

Correlation shows how any two values in a data are related to one another. A scatter plot is used to show correlation. One value is plotted along the X-axis while the other value is plotted along the Y-axis. The shape assumed by the dots on the scatter plot represents the relationship between the plotted values. The degree of their relationship is shown by a straight line, while a cloud appears for unrelated values.

**Regression**

Regression is an additional test that tells if two or more values in a datasets are related to each other. Regression has the property of interpolating one value of one factor from a value for the other that is not actually in the dataset. In most cases, regression measures two different factors such as income and the level of education and then establishes the relationship between the two distinct factors.

**Inferential Statistics**

Inferential statistics uses probability and the universal nature of normal curves to predict and make conclusions from a datasets. These characteristics are not visible by looking at the datasets directly. It predicts the similarity in two groups and tells the extent of that similarity. It tells how likely a value is a member of a group of other values

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