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- What is statistics
- Analytical software used in statistics
- Limitations of statistics
- Functions of statistics in real life situations
- Application of statistics
- Understanding probability distribution
- Historical development of statistics
- Understanding measures of variability
- Statistical hypothesis testing
- Differences between descriptive and inferential statistics
To understand the statistics from a holistic point of view, every student should understand the two broad branches of statistics. Often, the types of work we do hide many aspects of statistics. However, it is essential to understand the whole idea of statistical analysis for you to feel the beauty of it. The two branches of statistics are descriptive statistics and inferential statistics. All these branches of statistics follow a specific scientific approach which makes them equally essential to every statistics student.
Descriptive Statistics
Descriptive statistics is considered as the first part of statistical analysis which deals with collection and presentation of data. Scientifically, descriptive statistics can be defined as brief explanatory coefficients that are used by statisticians to summarize a given data set. Generally, a data set can either represent a sample of a population or the entire populations. Descriptive statistics can be categorized into
- Measures of central tendency
- Measures of variability
To easily understand the analyzed data, both measures of tendency and measures of variability use tables, general discussions, and graphs.
Measures of Central Tendency
Measures of central tendency specifically help the statisticians to estimate the center of values distribution. These measures of tendency are:
- Mean
This is the conventional method used in describing central tendency. Usually, to compute an average of values, you add up all the values and then divide them with the number of values available.
- Median
This is the score found at the middle of a set of values. A simple way to calculate a median is to arrange the scores in numerical orders and then locate the score which is at the center of the arranged sample.
- Mode
This is the frequently occurring value in a given set of scores.
Measures of Variability
The measure of variability help statisticians to analyze the distribution spread out of a given set of data. Some of the examples of measures of variability include quartiles, range, variance and standard deviation.
Inferential Statistics
Inferential statistics are techniques that enable statisticians to use the gathered information from a sample to make inferences, decisions or predictions about a given population. Inferential statistics often talks in probability terms by using descriptive statistics. These techniques are majorly used by statisticians to analyze data, make estimates and draw conclusions from the limited information which is obtained by sampling and testing how reliable the estimates are.The different types of calculation of inferential statistics include:
- Regression analysis
- Analysis of variance (ANOVA)
- Analysis of covariance (ANCOVA)
- Statistical significance (t-test)
- Correlation analysis
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