Statistics

In my Physics class, we conduct two types of labs: verification labs and inquiry-based labs. (Click here for more information)

Verification labs typically require students to analyze their data by calculating percentage errors or percentage differences.

Inquiry-based labs involve a more detailed analysis using descriptive statistics. By Physics 12, students are expected to plot a histogram and determine key statistical measures, including the maximum, minimum, range, mean, median, and mode of their data. Additionally, they should calculate the standard deviation, skewness, and kurtosis to better describe the distribution of their data.

Students are also required to analyze the effects of removing outliers (the highest and lowest data points) to observe how this impacts their results.

In some cases, where appropriate, students are expected to perform a T-test or Z-test to further analyze their data. A T test should be done whenever the data does not exhibit the characteristics of a NORMAL distribution and a Z test should be done when the data does exhibit the characteristics of a NORMAL distribution 

Statistics is the study of collecting, analyzing, interpreting, and presenting data. It is a branch of applied mathematics focused on summarizing data. Two key concepts we will be focusing on in our class are uncertainty and variation, which can be understood through statistical analysis. Probability plays a major role in determining these uncertainties.

Qualitative or Categorical Data (These are generally not the type of data we will be collecting in our labs).

Qualitative (or categorical) data refers to information that fits into specific categories and is not numerical. This data describes qualities or characteristics, like a person's gender or hometown. Although sometimes numbers are used, they don't have a true mathematical meaning, such as birthdates or postal codes. 

• Nominal Data

Nominal data is a type of qualitative data used to label variables without any numerical value. It cannot be ordered or measured. Examples include names, symbols, colors, or gender. Nominal data is often grouped and analyzed by counting frequencies or percentages, and it can be represented visually with pie charts.

• Ordinal Data

Ordinal data has a natural order but the difference between values is not measurable. It is commonly found in surveys or questionnaires. Ordinal data is often represented using bar charts or tables where each row shows a distinct category.

Quantitative or Numerical Data (These are the type of data we collect in out labs).

Quantitative data represents numerical values, like how much or how many. Examples include height, weight, or size. It can be divided into two types: discrete and continuous data. 

• Discrete Data

Discrete data consists of distinct, separate values and cannot be subdivided meaningfully. For example, the number of students in a class is discrete.

• Continuous Data

Continuous data can take any value within a range, meaning it can be measured and subdivided infinitely. An example is temperature within a certain range.

I fully expect my students to perform these calculations using Excel or some other websites, apps or programs. The manual calculations of these values should have been covered in Math classes. As our "n" value in our inquiry-based labs are usually much higher than 30. Manual calculations of these values is simply too time consuming.