Today, statistical analysis and data science seem to rule the world. Almost every professional field is dependent on statistical analysis and good statisticians to get some work done. So, these people need to have a certain level of education about applied statistics. Also, you might want to confuse statistics with mathematics, but they are quite similar but different in many ways.

Statistics is one of the popular courses of study at the tertiary level of education in Nigeria. These national institutions of higher education have great statisticians teaching students basic and advanced level statistics.

In addition, people from other professions use statistical tests in their research and book statisticians to help them out. For example, people hire statisticians to help them understand the pattern of a particular group of data or track their industry's finance or financial status based on dependent and independent variables.

Many secondary schools in Nigeria have incorporated and united the study of statistics into their syllabus in the form of mathematics, where students study some of the fundamentals of statistics, namely mean median and mode.

However, other important statistical concepts might sound new to students and are above the level of basic secondary school education in Nigeria. They include variance, standard variation, cumulative frequency, range, regression, correlation and more.

We will be showing in simple terms what these basic statistical concepts mean.

  • Mean: It is the average value of a probability distribution, which is calculated as the sum of all observable variables over the number of observable variables.
  • Median: The midpoint of a group of data is calculated by arranging all observable variables from the lowest to the highest and taking the value directly from the middle.
  • Mode: It is the most occurring value in a distribution, meaning the value that frequently occurs in a distribution.
  • Range: This is the difference between the largest and smallest values in a set or group of data distribution, which helps estimate how wide the observable variables are.
  • Correlation: It is a statistical method that determines how the association between two independent variables affect a distribution.
Statistical charts
Statistical analysis is required in most professional fields today || Image Source: Pexels
  • Regression: It is a statistical method that helps to know the factors affecting distribution, factors that don't affect the distribution and how they influence each other. This refers to how both independent and dependent variables affect a distribution.
  • Variance: It is a measure of dispersion or variability that lets you know the degree to which there is a spread in your group of data. It is calculated by taking the average of the squared deviation from the mean.
  • Standard variation: This is another method of measuring dispersion and variation, which measures the amount of variability from the individual data values (independent variable) to the mean.

Before using any of these concepts, you must have certain data with which you are testing. For instance, walking around the market to know about a population makes less sense.

However, when you're provided with some information such as their age, gender, occupation, monthly income, monthly financial budget, what they tend to buy more from the market and so on, it will make it easier for you to analyse the population you're working with.

Here's a complete guide to learning data science.

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What Is Statistical Analysis and Probability?

Statistical analysis is the applied science of collecting data and discovering patterns and trends. It is usually based on research, predictions, probability, testing with different values, variable samples, testing of hypotheses, and lots more.

Probability

Probability is a major concept of statistics. It is referred to as the foundation of statistics on which other factors are based. It is defined as the number between values 0 and 1, expressing the actual likelihood of an event to occur. It deals with finding out the likelihood of the occurrence of an event.

Probability has been applied in different areas such as business, finance, projects, games, education, etc., to make predictions.

To get the probability of an event, you have to divide the favourable number of outcomes by the total possible outcome. You can use probability to predict outcomes from rolling a dice, drawing cards from a pack, and throwing a coin.

Examples of probabilities are:

  • If it will rain or not
  • If the outcome of throwing a coin will be head or tail
  • If the outcome of rolling a dice will be a six or other numbers on the die

We also have the probability distribution, which is a mathematical function that gives you the probability that an event will occur. A function in probability is a discrete and random variable that gives the probability that an outcome associated with a variable will occur.

From a probability function, we can get the probability of a given event at any point in time. It also helps to know how widely distributed a group of data is.

You may want to read about data analysis methods.

Statistics in Data Science

Big data
Statistical analysis is also used to make sense of big data || Image Source: Pexels

Statistics is involved in all the highly sophisticated machines and learning algorithms used in data science. Statistics helps to translate data patterns and trends into useful evidence. A data scientist uses it to collate, review, draw conclusions from a data sample and apply it to a real-life situation.

Data scientists are in high demand in almost every sector in our digital world, including the research industry, education, finance, and more. To become a good data scientist, you must be very familiar with statistics and know how to apply it to data analysis to yield meaningful results. However, if you are not familiar with statistics, learning or taking a course is ideal.

If you want to learn statistics for data analysis from the basic to the advanced level, you need to find an experienced statistics tutor. In case you do not know where to start, you can book a tutor via Superprof!

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Statistical Methods

These are methods for analysing data to check for certain patterns and trends. Statistical methods are also models and techniques used in data analysis. With statistical methods, you can get information from research data and provide different processing methods to get meaningful output.

There are two types of the statistical method used in analysing data:

Descriptive Statistics

This method is used to summarise data characteristics in a meaningful manner. It qualitatively describes features from a collected sample.

It involves central tendency (mean, median and mode), Measure of dispersion, also known as variability (range, variance, standard deviation) and more.

Inferential Statistics

This statistics involves taking data from your samples to generalise a population. With Inferential statistics, you can conclude even about components outside the data you're working with.

For instance, you can enter any restaurant in Nigeria and ask 20 people what food they will be ordering. This will help you conclude what food everyone in that restaurant is likely to order.

Types of Inferential statistics:

  • Hypothesis test
  • Pearson correlation
  • Confidence interval
  • Bivariate regression
  • Multivariate regression
  • Anova

How to Perform a Statistical Analysis

Performing statistical experiments sounds like much fun, but there are many things attached to the different statistical tests you carry out. However, this is usually based on the type of data you're working with.

To run a test accurately, you must be predictive and accurate with your predictions. It would be best to consider some factors before going deeply into your data analysis. These factors will help you determine the appropriate statistical tool for your study. These factors include:

  • The reason behind your study
  • The type and distribution of data you'll be using
  • The nature of observation, either paired or unpaired

To go ahead with your analysis, you need to know certain things that will aid you as you proceed with your data analysis.

  • You need to write out your hypothesis
  • Collect the data needed from the sample you're working on
  • Draft out the summary of your data with the aid of descriptive analysis
  • Test your hypothesis to see if they match
  • You can now proceed to interpret your results.
Python codes
The Python programming language is one of the best tools for data science || Image Source: Pexels

However, there are different programs or software that you can use to run a statistical analysis smoothly. They include:

  • Excel
  • Python
  • MATLAB
  • SQL
  • Stata
  • SAS
  • SPSS
  • JMP and more.

You might seem a little bit confused about all these concepts, don't worry because there are different online platforms such as Superprof, where you can get teaching services and book a course for better knowledge. Learning has never been easier!

Application of Statistical Analysis

Statistics provides data samples alongside tools that you will use for carrying out the data analysis and tests. It is a very effective tool that can be used when it comes to decision making. Also, statistics are used in the finance industry, national matters, business, research, education, and more!

Statistical analysis helps you collect data from a sample, test, research and explore these data to discover certain patterns and trends. It is used to solve complex problems in the real world.

Industries use statistics majorly to improve the quality of their goods and services. To achieve this, they use statistics to make sales projections and financial analyses of capital for projects.

They also use it to analyse past project performance and predict the future of both new and old products and business practices.

All of these shows that statistics will be around for a while. If you would love to jump on this new train, check out the Superprof website and search for a suitable teacher to offer you a statistical analysis course!

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Emmanuel

Emmanuel Oyibo is a Content Writer and budding Python Developer. He's adept at many niches, but his expertise shines more in the medical sciences, tech, and blockchain niches. Emmanuel has a Bachelor's degree in Biochemistry, and he's currently a full-time freelance content writer. When he's not working, he's probably absorbed in some television adventure series or a good book.