# Statistical Analysis

Statistical analysis is the process of collecting, cleaning, and organizing data to extract meaningful insights. It involves using statistical methods to summarize, model, and interpret data. Statistical analysis is used in a wide variety of fields, including science, medicine, engineering, business, and social sciences.

The following are some of the common steps involved in statistical analysis:

1. Data collection: The first step is to collect the data that you want to analyze. This data can be collected from a variety of sources, such as surveys, experiments, or observational studies.
2. Data cleaning: Once you have collected your data, you need to clean it to remove any errors or inconsistencies. This may involve removing duplicate data, correcting typos, and filling in missing values.
3. Data organization: Once your data is clean, you need to organize it in a way that is easy to analyze. This may involve creating tables, charts, or graphs.
4. Exploratory data analysis: Exploratory data analysis (EDA) is the process of using statistical methods to explore and understand your data. This may involve calculating summary statistics, creating visualizations, and testing for correlations.
5. Statistical modeling: Statistical modeling is the process of using statistical methods to build models of your data. These models can be used to predict future outcomes, identify relationships between variables, or test hypotheses.
6. Model evaluation: Once you have built a statistical model, you need to evaluate its performance. This involves testing the model on new data to see how well it predicts the outcomes.
7. Model interpretation: Once you have evaluated your model, you need to interpret its results. This involves explaining what the model means and how it can be used to make inferences about your data.

Statistical analysis is a powerful tool that can be used to extract valuable insights from data. However, it is important to use statistical methods correctly in order to avoid making errors or drawing false conclusions.

Discover How To Get Started Assessing Statistical Analysis Skills

## Our Customers Say

We get a high flow of applicants, which leads to potentially longer lead times, causing delays in the pipelines which can lead to missing out on good candidates. Alooba supports both speed and quality. The speed to return to candidates gives us a competitive advantage. Alooba provides a higher level of confidence in the people coming through the pipeline with less time spent interviewing unqualified candidates.

Scott Crowe, Canva (Lead Recruiter - Data)