Achievement Standard 1.1: Statistical Inquiry Process

The specific question or statement that drives the investigation. It clarifies the purpose and helps define the focus.

Explaining the origin of the data used in the investigation, whether it was collected directly or obtained from another source.

Describing the methods used to collect data if it was gathered firsthand. It includes the rationale behind the specific information chosen and how it was obtained.

Explaining the source of existing data and its reliability when using information gathered by others. It involves identifying the origin of the data and assessing its trustworthiness.

Examining factors that influence the quality and reliability of the data used in the investigation. It involves identifying potential biases or limitations in the data and its collection methods.

Using the data to address the investigative question or statement with a comprehensive and thoughtful conclusion that considers the context of the investigation.

Demonstrating an understanding of the context of the investigation, including how the data relates to the real world. This shows how the findings can be applied or interpreted in a meaningful way.

Using statistical concepts and methods to analyze the data and draw conclusions. This includes using charts, graphs, and calculations to interpret the data and identify patterns or trends.

Data that can be measured numerically and has a continuous range of values. Examples include height, weight, and temperature.

Data that falls into distinct categories or groups. Examples include gender, hair color, and favorite fruit.

The context of the data helps understand its meaning. It includes details about how the data was collected, the variables involved, and the purpose of the data.

Variations in data can occur due to different factors. These variations can be natural or caused by the data collection process.

Managing variations in data involves deciding how to address them. This could include choosing appropriate data collection methods or applying statistical techniques to account for the variations.

Visual representations of data, such as graphs and charts, are used to explore and understand relationships between variables.

Numerical summaries or calculations that describe data. Examples include mean, median, and standard deviation.

The relationship between two numeric variables is investigated using statistical methods. This involves exploring how the values of one variable change in relation to the values of the other variable.

The overall direction of data points on a scatter graph, either positive or negative. Positive relationships have points sloping upwards from left to right, while negative ones slope downwards. Direction is determined by observing the overall trend of the data points and describing it as positive or negative.

The closeness of data points to an imaginary trend line. Think about how scattered or tightly clustered the points are. Strong relationships have points tightly clustered around a line. Weak relationships have widely scattered points.

A group of data points on a scatter graph that are clustered closely together. This can indicate a specific trend or relationship within a smaller segment of the data.

A data point that significantly stands out from the main trend of the data, possibly indicating an outlier or unusual instance. Anomalies should be identified and explained, adding context to the overall relationship.

On a scatter graph, this refers to how strong or weak the relationship appears to be. It helps you understand whether there's a clear connection between the two variables being plotted. Visual indicators include the shape of the data points (ovals, brushstrokes, etc.).

Differences in measurements caused by factors like natural changes in a variable or inconsistencies in the data collection process.

How well the data represents the real-world phenomenon being studied.

Variation that happens naturally in the object or phenomenon being measured.

Variation caused by differences in the conditions or procedures used to collect data.

Variations that occur from one measurement to another, even when measuring the same thing.

Variations that occur due to the limitations of the measurement tools or methods used.

Examining the spread of data points, patterns, and trends to gain insights from the data.

A line of best fit represents the general trend of data points on a graph. It's not always a perfect fit, but it captures the overall relationship.

A prediction is an educated guess about a future value based on existing data.

Visual inspection is a way to analyze a graph by looking at its overall shape and trends to make predictions.

Substituting values into the equation of the line of best fit can be a more reliable way to make predictions, especially if the equation is provided.

Exemplar marking schedules provide examples of possible student responses to help understand the desired level of performance.

Teacher Guidance provides resources for educators, including exemplar marking schedules, to support their teaching and assessment.

Manually adding 'train tracks' to a graph represents the uncertainty or margin of error in prediction.

Exemplar responses are not definitive expectations for a standard, but they illustrate what a student may achieve at a particular level.

Data points that are significantly different from the rest of the data set. They could be unusually high or low values, outliers, or unexpected trends.

A group or collection of similar data points. They show clustering or grouping within the data set.

The process of using a sample of data to draw inferences or conclusions about a larger population. This is crucial for generalizing findings to a wider group.

Determining if the patterns observed in a sample dataset likely exist in the larger population.

Examining if the middle 50% sections of two groups of data overlap or are separated. Used to make a call about population trends.

Using the ¾ - ½ rule to compare two data sets in samples of size 20-40. This helps determine if the central tendency is different between the groups.

Using the distance between the medians as a proportion of the overall visible spread. This is used for sample sizes of 100 or more to make sample to population inferences.