## What is Statistical Analysis?

Statistical analysis involves assessing quantitative data to identify data characteristics, trends, and relationships. Scrolling through the raw values in a dataset provides virtually no useful information. Statistical analysis takes the raw data and provides insights into what the data mean. This process can improve understanding of the subject area by testing hypotheses, producing actionable results leading to improved outcomes, and making predictions, amongst many others.

Statistical analysis can help you understand quantitative research, experiments, surveys, manufacturing, and business data. Correspondingly, a wide range of people perform these analyses, including businesses, scientists, and governments, because they can use the analytical results to make objective, data-driven decisions. Ideally, these techniques take a sea of raw data and produce easily understandable results.

The field of statistics is the science of learning from data, and it studies that process from beginning to end to understand how to produce trustworthy results. While statistical analysis provides tremendous benefits, obtaining valid results requires proper methods for collecting your sample, taking measurements, designing experiments, and using appropriate analytical techniques. Consequently, data analysts must carefully plan and understand the entire process, from data collection to statistical analysis. Alternatively, if someone else collected the data, the analyst must understand that context to interpret their results correctly.

If you’re good with numbers and enjoy working with data, statistical analysis could be the perfect career path for you. As big data, machine learning, and technology grow, the demand for skilled statistical analysts is rising. It’s a great time to build these skills and find a job that fits your interests.

Some of the fastest-growing career paths use statistical analysis, such as statisticians, data analysts, and data engineers. In this post, you’ll learn the different types of statistical analysis, their benefits, and the key steps.

## Types of Statistical Analysis

Given the broad range of uses for statistical analysis, there are several broad categories. These include descriptive, inferential, experimental, and predictive statistics. While the goals of these approaches differ, they all aim to take the raw data and turn them into meaningful information that helps you understand the subject area and make decisions.

Choosing the correct approach to bring the data to life is an essential part of the craft of statistical analysis. For all the following types of statistical analyses, you’ll use statistical reports, graphs, and tables to explain the results to others.

### Descriptive

Descriptive statistical analysis describes a sample of data using various summary statistics such as measures of central tendency, variability, relative standing, and correlation. These results apply only to the items or people that the researchers measure and not to a broader population. Additionally, correlations do not necessarily imply causation.

For example, you can report the mean test score and the correlation between hours of studying and test scores for a specific class. These results apply only to this class and no one else. Do not assume the correlation implies causation.

### Inferential

Inferential statistical analysis goes a step further and uses a representative sample to estimate the properties of an entire population. A sample is a subset of the population. Usually, populations are so large that it’s impossible to measure everyone in a population.

For example, if you draw a simple random sample of students and administer a test, statistical analysis of the data allows you to estimate the properties of the population. Statistical analysis in the form of hypothesis testing can determine whether the effects and correlations you observe in the sample also exist in the population.

Suppose the hypothesis test results for the correlation between hours studying and test score is statistically significant. In that case, you can conclude that the correlation you see in the sample also exists in the larger population. Despite being statistically significant, the correlation still does not imply causation because the researchers did not use an experimental design.

The sample correlation estimates the population correlation. However, because you didn’t measure everyone in the population, you must account for sampling error by applying a margin of error around the sample estimate using a confidence interval.

Learn more about the Differences between Descriptive and Inferential Statistical Analysis.

### Designed Experiments

Statistical analysis of experimental designs strives to identify *causal* relationships between variables rather than mere correlation. Observing a correlation in inferential statistics does not suggest a causal relationship exists. You must design an experiment to evaluate causality. Typically, this process involves randomly assigning subjects to treatment and control groups.

Does increasing study hours *cause* test scores to improve or not? Without a designed experiment, you can’t rule out the possibility that a confounding variable and not studying caused the test scores to improve.

Suppose you randomly assign students to high and low study-time experimental groups. The statistical analysis indicates the longer-duration study group has a higher mean score than the shorter-duration group. The difference is statistically significant. These results provide evidence that study time causes changes in the test scores.

Learn more about Correlation vs. Causation and Experimental Design: Definition and Types.

### Predictive

Predictive statistical analysis doesn’t necessarily strive to understand why and how one variable affects another. Instead, it predicts outcomes as precisely as possible. These analyses can use causal or non-causal correlations to predict outcomes.

For example, assume that the number of ice cream cones consumed predicts the number of shark attacks in a beach town. The correlation is not causal because ice cream cone consumption doesn’t cause shark attacks. However, the number of cones sold reflects favorable weather conditions and the number of beachgoers. Those variables do cause changes in the number of shark attacks. If the number of cones is easier to measure and predicts shark attacks better than other measures, it’s a good predictive model.

## Three Key Steps in Statistical Analysis

Producing trustworthy statistical analysis requires following several key steps. Each phase plays a vital role in ensuring that the data collected is accurate, the methods are sound, and the results are reliable. From careful planning to adequate sampling and insightful data analysis, these steps help researchers and businesses make informed, data-driven decisions. Below, I outline the major steps involved in conducting solid statistical analysis.

### Planning

The planning step is essential for creating well-structured experiments and studies that effectively set up the statistical analysis from the start. Whether working in a lab, conducting fieldwork, or designing surveys, this stage ensures that the research design gathers data that statistical analysis can use to answer the research question effectively. Researchers can make informed decisions about variables, sample sizes, and sampling methods by analyzing data patterns and previous statistical analyses. Using the proper sampling techniques allows researchers to work with a manageable portion of the population while maintaining accuracy.

This careful preparation reduces errors and saves resources, leading to more reliable results. Optimizing research strategies during the planning phase allows scientists and businesses to focus on the most relevant aspects of their investigations, which results in more precise findings. Researchers design the best studies when they keep the statistical analysis in mind.

Learn more about Planning Sample Sizes and Sampling Methods: Different Types in Research.

### Data Collection

After all the planning, the next step is to go out and execute the plan. This process involves collecting the sample and taking the measurements. It might also require implementing the treatment under controlled conditions if it’s an experiment.

For studies that use data collected by others, the researchers must acquire, prepare, and clean the data before performing the statistical analysis. In this context, a crucial part of sound statistical analysis is understanding how the data were gathered. Analysts must review the methods used in data collection to identify potential sampling biases or errors that could affect the results. Sampling techniques, data sources, and collection conditions can all introduce variability or skew the data. Without this awareness, an analyst risks drawing flawed conclusions.

Analysts can adjust their approach and account for any limitations by carefully examining how the data was collected, ensuring their statistical analysis remains accurate and trustworthy.

### Statistical Analysis

After data collection, the statistical analysis step transforms raw data into meaningful insights that can inform real-world decisions. Ideally, the preceding steps have all set the stage for this analysis.

Successful research plans and their effective execution allow statistical analysis to produce clear, understandable results, making it easy to identify trends, draw conclusions, and forecast outcomes. These insights not only clarify current conditions but also help anticipate future developments. Statistical analysis drives business strategies and scientific advancements by converting data into actionable information.

Learn more about Hypothesis Testing and Regression Analysis.

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