Introduction to Statistics: What It Is, Why It Matters and Its Main Types
Every day we are surrounded by numbers: the price of petrol, the chance of rain this afternoon, the number of dengue cases in our district, or the CGPA on a transcript. Behind each of these numbers is statistics. In this post we will look at what statistics is, how it is used in real life, and the two main types of statistics that every beginner should know.
What is statistics?
Statistics is the science of collecting, organising, analysing, interpreting and presenting data. Its purpose is to help us draw conclusions and make decisions when we face uncertainty, that is, when we cannot observe everything or when the outcome is not yet known.
The word is used in two ways. As a discipline, statistics refers to the methods for working with data. As plural numbers, statistics are the figures produced from data, such as the unemployment rate, average household income or a course pass rate.
The statistical process in five steps:
Collect – gather data through surveys, experiments, sensors or existing records.
Organise – clean the data and arrange it in tables or spreadsheets.
Analyse – calculate summaries and fit statistical models.
Interpret – explain what the results mean for the question being asked.
Present – communicate the findings with charts, tables and reports.
Put simply, statistics turns data into information, and information into decisions.
The role of statistics in real life
Statistics is not just a subject in the classroom. It quietly supports decisions in almost every field. Here are some everyday examples:
Health and medicine: Before a new drug is approved, clinical trials compare patients who receive the drug with those who receive a placebo. Statistical tests show whether the improvement is real or just due to chance. Health authorities also track weekly dengue and influenza cases to detect outbreaks early.
Business and marketing: Companies analyse past sales to forecast demand and decide how much stock to order, and use customer surveys to measure satisfaction. An online shop may run an A/B test to see which website design leads to more purchases.
Weather forecasting: When the forecast says there is a 70% chance of rain, it means that on past days with similar atmospheric conditions, rain fell about 7 times out of 10.
Government and public policy: The population and housing census conducted by the Department of Statistics Malaysia (DOSM) tells the government how many people live in each area, along with their age, education and household characteristics. This guides where to build schools, clinics and roads.
Education: Lecturers and teachers analyse exam results to find topics students struggle with, compare performance across classes, and set fair grade boundaries.
Finance and insurance: Investors study the trend and volatility of gold prices or the stock market, while insurance companies use data on accidents and life expectancy to set premiums.
Sports: Coaches use player statistics such as shooting percentage or distance covered to choose tactics and line-ups.
In each case, statistics helps people move from guessing to making evidence-based decisions.
Types of statistics
Statistics is commonly divided into two main branches: descriptive statistics and inferential statistics.
1. Descriptive statistics
Descriptive statistics summarise and describe the data you actually have. They do not try to go beyond the data. Common tools include:
Measures of central tendency: mean, median and mode
Measures of dispersion: range, variance, standard deviation and interquartile range
Tables and graphs: frequency tables, bar charts, pie charts, histograms and box plots
Example: A lecturer reports that the average mark of 40 students in a class is 72, with a standard deviation of 8, and shows the distribution of marks in a histogram. This describes that particular class only.
2. Inferential statistics
Inferential statistics use data from a sample to draw conclusions about a larger population. Because we rarely have data on everyone, we study a smaller group and use probability to generalise, while stating how confident we are in the result. Common tools include:
Estimation and confidence intervals
Hypothesis testing, such as the t-test and chi-square test
Correlation, regression and prediction models
Example: A survey of 1,000 randomly selected voters finds that 52% support a candidate. Using inferential statistics, we can estimate the support among all voters, for instance 52% ± 3% with 95% confidence.
Two key terms to remember: the population is the entire group we want to learn about, while the sample is the subset of the population that we actually observe. A good sample should be representative of the population, which is usually achieved through random sampling.
Summary
Statistics is the science of learning from data. It helps doctors test treatments, businesses plan ahead, governments allocate resources, and all of us make sense of the numbers we see every day. Descriptive statistics tell us what the data show; inferential statistics help us draw conclusions beyond the data we have. The sketchnote below summarises the key ideas of this article.
Feel free to save and share this sketchnote with your students and friends who are starting their statistics journey.
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