Thread by @selcukorkmaz

1/ 📊📏 Let's dive into the world of #statistics & explore the Levels of Measurement! Understanding these levels is crucial for choosing the right statistical methods for data analysis. Today, we'll cover the 4 main levels: Nominal, Ordinal, Interval, and Ratio. #DataScience

2/ 🏷️ Nominal Level: At this level, data is purely qualitative and categorical. There's no inherent order or ranking involved. Examples include colors, genders, or nationalities. It's important to note that mathematical operations like addition or subtraction don't apply here.

3/ 🥇🥈🥉 Ordinal Level: This level involves data that has an inherent order or ranking, but the difference between categories is not uniform. Examples include survey responses (Strongly Disagree to Strongly Agree) or educational levels (elementary, high school, college).

4/ 📏🌡️ Interval Level: With interval data, there's a consistent and measurable difference between categories, but there's no true zero point. Examples include temperature in Celsius/Fahrenheit, or historical years (BCE/CE). Keep in mind, ratios don't make sense here.

5/ 🔢 Ratio Level: The highest level of measurement, ratio data, has all the properties of interval data, but with a true zero point. Examples include weight, height, or age. At this level, all mathematical operations can be performed, including ratios.

6/ 📊💡 To choose the right statistical methods for data analysis, understanding the level of measurement is essential. Each level of measurement has its appropriate descriptive and inferential statistics. This ensures accurate interpretation of data.

7/ 🧮🔎 Remember: Different levels of measurement call for different statistical tests. For example, Chi-square tests are used for nominal data, while t-tests and ANOVA are applicable for interval and ratio data. Keep the level of measurement in mind when analyzing data!

8/ 🎓 In summary, the Levels of Measurement in #statistics are crucial for understanding the nature of data and selecting the appropriate methods for analysis. Nominal, Ordinal, Interval, and Ratio levels each have their own unique properties and applications. #DataScience

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