The next step in your research project after data collection is data analysis. Quantitative data analysis involves systematically using statistical methods to examine and interpret numerical data. The aim of statistical analysis is to reveal patterns and trends within a data set. The raw data is reduced during analysis and transformed into actionable conclusions and recommendations. Six simple steps are key to analyzing data: (1) preparing data for analysis, (2) defining the analysis objectives, (3) selecting variables for use in analysis, (4) data exploration and descriptive analysis, (5) confirmatory or inferential data analysis, and (6) interpretation and reporting. Preparing data for analysis involves data entry, variable labeling, cleaning, and organizing data into a matrix to facilitate analysis. Defining analysis objectives is key to determining the type of analysis required. Once the data is well-organized, data exploration and descriptive statistics are done. The objective is to summarize the data using frequencies, measures of central tendency (MoT), and measures of variability (MoV). Measures of central tendency include the mode, median, and mean, while measures of variability include range, percentiles, quartiles and box plots, coefficient of variation, standard deviation, and scatter plots. The normal distribution curve forms the foundation of many statistical tests due to its predictive nature. A pivot table is a useful tool for summarizing, organizing, and analyzing data and can easily be done through a drag-and-drop interface in Excel.