Statistics

• What is the statistical enquiry cycle?

  A cycle for statistical investigations

  
• Why is the statistical enquiry cycle considered iterative?

  Steps are repeated with improvements
• What are the five stages of the statistical enquiry cycle?

  1. Hypothesis and Planning
  2. Collecting Data
  3. Processing and Representing Data
  4. Interpreting Results
  5. Evaluating

  
• What should be specified in the hypothesis and planning stage?

  A hypothesis to be investigated
• What is important when planning data collection?

  Minimizing bias in data collection
• What type of data should be collected in the collecting data stage?

  Primary data using an appropriate method
• Why is it important to clean the data?

  To ensure accuracy and reliability
• How should data be represented after processing?

  Using diagrams, graphs, or tables
• What should be considered when interpreting results?

  Context of the investigation
• What should be identified in the evaluating stage?

  Possible issues with data collection
• What is a hypothesis?

  A statement to be tested using statistics
• When should a hypothesis be stated?

  At the start of a statistical enquiry
• What is a constraint in a statistical investigation?

  Practical limits affecting the investigation
• What are some examples of constraints?

  Time, cost, and ethical issues
• Why is confidentiality important in data collection?

  To protect participants' private information
• What does sensitivity refer to in data collection?

  Discomfort discussing sensitive topics
• What does being proactive mean in the statistical enquiry process?

  Acting ahead of time to prevent issues
• What is raw data?

  Data in its original collected form
• What is the difference between quantitative and qualitative data?

  Quantitative can be numbers; qualitative cannot
• What is continuous data?

  Data that can take any numerical value
• What is discrete data?

  Data that can only take specific values
• What is categorical data?

  Data organized into non-overlapping categories
• What is ordinal data?

  Categorical data that can be ordered
• What is bivariate data?

  Data collected as pairs of values
• What is multivariate data?

  Data collected in sets of more than two values
• What is primary data?

  Data collected by the user or for the user
• What are the advantages of primary data?

  Specific to the question and known accuracy
• What are the disadvantages of primary data?

  Time-consuming and potentially expensive
• What is secondary data?

  Data collected by someone else
• What are the advantages of secondary data?

  Quicker, easier, and less expensive to obtain
• What are the disadvantages of secondary data?

  May be outdated or unreliable
• What must be acknowledged when using secondary data?

  The source of the data
• What type of data is described as quantitative?

  Data that can be recorded as a number
• What are examples of secondary data sources?

  Internet, print media, databases, research articles
• What is an advantage of using secondary data?

  It can be quicker and easier to obtain
• What is a disadvantage of secondary data?

  Data may be out of date or irrelevant
• What must you do when using secondary data?

  Acknowledge the source of the data
• What are the types of data described in the examples?

  1. Weights of dogs: quantitative, continuous
  2. Favourite ice cream flavours: qualitative
  3. Number of computers: quantitative, discrete
• Why is it okay to leave small data sets ungrouped?

  They are manageable and easy to analyze
• What is qualitative data?

  Data that cannot be recorded as numbers