Statistics
Statistics
Statistics is the field concerned with collecting, organising, describing, and analysing of data. This data is then interpreted and presented, and conclusions are made from it. The discipline is used in almost every industry, especially with the availability of data nowadays.
Statisticians apply statistical approaches in order to find patterns in data, and the typical steps to do so are as follows:
- Collect data (data could be from various sources and in different formats)
- Analyse data (clean, describe, prepare, and visualise data)
- Conclude findings (interpretation of data and decision making)
The field of statistics splits into two categories: descriptive and inferential.
Descriptive statistics
Descriptive statistics is a simpler informational technique, and its purpose is to describe or summarise the features (or characteristics) of data. Examples are:
- Highest average scoring of a football player
- Average default products in a factory in a single week
- Frequency of each activity in a leisure centre
- Spread of physics test scores in a college class
There are 3 types (measures) of descriptive statistics: central tendency, frequency distribution, and variability (dispersion).
- Central tendency focuses on the center (average) of a data set. It uses three main techniques for measurement: mean, median, and mode.
- Frequency distribution measures the occurrence of data points within a dataset, or how often something appears.
- Variability (dispersion) aims to identify how spread (dispersed) is the data. There are three main measurements tools: range, variance, and standard deviation.
Inferential statistics
Inferential statistics focus on making predictions about a dataset and drawing conclusions from it. Normally, data is collected (sampled) and the outcomes (findings) based on the sampled data help with understanding the bigger picture (or larger population). For instance:
- Estimating university grades from a randomly selected sample
- Finding the relationship of parents’ incomes and their children’s final grades
- Concluding whether employee training increases sales
- Understanding customers’ tastes and preferences by surveying a local sample of residents
There are 3 types (measures) of inferential statistics: hypothesis testing, confidence intervals. and regression analysis.
- Hypothesis testing tests different hypotheses from a sample of the population It measures how valid the findings are and whether the results happened by chance. Tools: Z-test, T-test, and F-test.
- Confidence interval estimates the population parameters. It tests where the population estimate lies and with how much confidence.
- Regression analysis measure the relationship and change of variables with respect to others. Tools: linear regression, logistic regression, nominal regression, ordinal regression, and multi-linear regression.
Next: Basic Statistics
