Interpreting Data

Understanding Datasets

A dataset is a collection of data, usually presented in a table or spreadsheet.
Variables are the different pieces of data in a dataset. They can be qualitative (categorical) or quantitative (numerical).
Categories pertain to names or labels, while values relate to numerical data.
Variables can be discrete, with distinct values, or continuous, taking any value within a certain range.

Univariate data involve one variable. For instance, height of humans.
Bivariate data involve two variables that may be related, such as age and weight.
Multivariate data include more than two variables. An example would be age, weight, and height.
Understanding the type of data you’re working with is crucial for its correct interpretation.

Pie Charts represent categorical data. Each segment shows a category’s percentage of the total.
Bar Charts make comparisons between categories. Both horizontal and vertical bars can be used.
Histograms present numerical data over an interval, and the area of each bar corresponds to the frequency of data.
Line Graphs are ideal for showing changes over time.
Scatter Plots assist in identifying relationships or correlation between two numeric variables.

Mean is the average of the data set.
The median is the middle point when the data are ordered from smallest to largest.
The mode is the most frequent data point.
Range is the difference between the highest and the lowest data points.
The quartiles split the ordered data set into four equal parts.
The interquartile range (IQR) is found by subtracting the lower quartile from the upper quartile.

Determine the trend in the data. Is it increasing, decreasing or staying the same?
Identify any outliers or data points that are far from the others. These can greatly impact the mean and range.
Assess variability by examining how spread out the data is. High variability indicates the data is spread far from the mean.
Look out for any patterns or relationships, such as a correlation between two variables.
Evaluate the significance of the data by understanding what it represents and its relevance to the given context.
Regularly question: “What does the data tell us?” to ensure a thorough understanding.