There Are Many Kinds Of Data.
Data collection may be difficult because of all the variables to consider. If you do not collect it yourself, it is important to understand that there are many data types that you can use and each one is applicable for different situations.
Probabilities are that you think of quantitative data as data. It is based upon precise measurements and is often analyzed by statistical methods.
Quantitative information, also known as numeric data can be presented in different formats.
The discrete data may be counted and broken down into smaller groups, like the number in a group. continuous data also exists on a continuum. For example, length. There are two types of interval data, which has no "zero", but ratio, which does, like weight. Line graphs can often be used to visualize continuous data such as profit and growth reports.
Qualitative on the other side is descriptive data, which is based on observations and cannot be measured. The analysis of this type of data involves the classification into themes and patterns based on particular characteristics. qualitative data can also be called.
There are two types of categorical statistics. The nominal data is useful for measuring frequencies and percentages. It can also be displayed in a chart or bar chart. Ordinal Data means that the data can be arranged in a logical manner (such as breakfast lunch and dinner).
Sometimes both qualitative, as well as quantitative data, can be collected and analyzed. If you're using this type, it may be possible to interpret them in different ways.
All of this to say: Understanding the type of data that you work with can help you decide how to best communicate it.
Data Can Often Tell Many Stories.
You are looking for patterns when you analyze data. These patterns might tell you something about what has occurred, what's the most common, or how it all fits together.
Most patterns are based on the relationships between two variables. These variables are the "things", that are being described, measured, or counted. Here are a few examples.
People who belong to bowling leagues will be more successful than those who are part of tennis teams.
Grocery stores in low income areas and grocery stores in high-income places (note: geographical data also includes map coordinates)
The way people feel about cats is very different from how they feel about their dogs
In the data, you may find many patterns for any one of these cases. Let's now look at the final example. It is possible to see that different people have different opinions. For example, people with children might like dogs more than those who are older.
Data isn't enough. Only by analyzing data can you uncover insights. There are many things to find, so don't settle for the first one you come across. Outliers and anomalies are data points that have significantly different data points and can indicate errors or areas for further analysis and study. As you conduct your analysis, be aware of your cognitive biases as well as possible pitfalls.
If You Are Proud Of Your Story, Please Share It Responsibly
• First, ask yourself what you want to share with others.
• Descriptions Data, such as frequency or percentages
• The distributions of data, such as averages or ranges
• Comparisons in data, such as time changes or correlations between variables
Another problem is to try and equate correlation with causal. Although there might be some correlation between sunscreen sales & ice cream sales it doesn't mean that the sales will increase if more ice cream is sold. This could mean that both are due to a third variable: time of year.
Once you know your message, you can choose the visual that best helps someone else to understand.
You Don’t Have To Know Everything
But, you need to continue learning! There's so much to learn! If you need to help prioritize what to do first, it's crucial to understand how data could be misused to mislead. You must ensure that you are not doing this.
You know how it was that reading was the best way you could learn to read. Now, it is the best thing to increase your data literacy consulting by sharing and working with data. It will be easy to see the immense value of sharing your data with others.