Statistics in Health Sciences

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Questions and Answers

Which of the following is an example of discrete data?

  • Height of a child
  • Amount of rain in inches
  • Weight of a truck
  • Number of children in a class (correct)

What characteristic distinguishes continuous data from discrete data?

  • Discrete data cannot include categorical values.
  • Continuous data values can be subdivided. (correct)
  • Discrete data has clear gaps between values.
  • Continuous data can only be whole numbers.

Which type of data is used to label variables without providing a quantitative value?

  • Ordinal Data
  • Nominal Data (correct)
  • Ratio Data
  • Continuous Data

When is data considered ordinal?

<p>When it includes categories that can be ranked. (C)</p> Signup and view all the answers

Which of the following statements about discrete data is FALSE?

<p>Discrete data can have decimal values. (C)</p> Signup and view all the answers

Which of the following is NOT a feature of continuous data?

<p>Can only be whole numbers (C)</p> Signup and view all the answers

What would be an example of continuous data?

<p>The cholesterol levels of adults (D)</p> Signup and view all the answers

Which of the following pairs represent qualitative data types?

<p>Nominal and Ordinal Data (D)</p> Signup and view all the answers

What is one of the main purposes of statistics in health sciences?

<p>To create health-related decisions based on analysis (C)</p> Signup and view all the answers

Which of the following best describes continuous data?

<p>Numerical data that can take on any value within a range (A)</p> Signup and view all the answers

What type of variable would 'blood sugar level' be classified as in a statistical experiment?

<p>Dependent variable (A)</p> Signup and view all the answers

Which of the following is NOT a basic terminology in statistics?

<p>Random variables (D)</p> Signup and view all the answers

Which of the following statements about qualitative data is true?

<p>It is descriptive in nature and does not involve numbers. (B)</p> Signup and view all the answers

What is considered as raw information collected for analysis in statistics?

<p>Data (C)</p> Signup and view all the answers

Which statement best reflects the role of descriptive statistics?

<p>They summarize and explain the main features of a dataset. (C)</p> Signup and view all the answers

Discrete data can be defined as:

<p>Countable data that can only take specific values. (D)</p> Signup and view all the answers

Flashcards

Quantitative data

A type of variable that can be measured numerically. It can be counted or measured. Examples are age, height, weight, and blood pressure.

Qualitative data

A type of variable that describes qualities or characteristics. It cannot be measured numerically. Examples are color, gender, and opinion.

Data

Information or facts gathered for analysis. It can be quantitative or qualitative.

Independent variable

A variable that is controlled or changed by the researcher. Its changes are observed to see their effect on the dependent variable.

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Dependent variable

A variable that is measured or observed in response to changes in the independent variable. Its changes are the result of the independent variable.

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Descriptive statistics

Statistics that describe and summarize a dataset. It focuses on presenting data in a meaningful way.

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Inferential statistics

Statistics that infer conclusions about a population based on a sample of data. It uses probability to generalize findings to a larger group.

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Discrete data

A variable that can take on a limited number of distinct values. It can be counted but not measured. Examples are the number of children in a family, or the number of red cars in a parking lot.

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Continuous Data

Data that has values that can be measured and can be divided into smaller units, allowing for decimal points.

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Nominal Data

A type of data that uses labels or categories to group data, without any inherent order or ranking.

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Ordinal Data

A type of data that groups data into categories that have a natural order or ranking, but the difference between categories may not be equal.

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Ratio Data

Data that can be measured and has a meaningful zero point, allowing for ratios and proportions.

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Interval Data

Data that can be measured and has intervals between values, but no true zero point.

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Study Notes

Statistics in Health Sciences

  • Statistics is the science of collecting, describing, analyzing, interpreting data, and drawing conclusions for decision-making.
  • In health sciences, statistics focuses on collecting, analyzing, presenting, and interpreting health-related data to make informed decisions.

Data Types

  • Data: Raw information and facts collected for analysis, categorized as qualitative or quantitative.
  • Quantitative Data: Numerical data (counts, numbers, measurements).
    • Discrete/Categorical Data: Distinct, separate whole numbers (e.g., number of patients, test scores). Cannot have decimal points.
      • Examples: Number of students in a class, number of hospitalizations, shoe sizes.
      • Medical Examples: Number of needle punctures, number of pregnancies.
      • Levels of Measurement:
        • Nominal: Labels variables without quantitative value (e.g., gender, ethnicity).
        • Ordinal: Variables in naturally ordered categories (e.g., ranking, rating scales).
    • Continuous Data: Values that can be subdivided into smaller pieces; measurable with infinitely many possible values (e.g., height, weight, time).
      • Examples: Height of children, blood pressure, cholesterol level.
      • Medical Examples: Body mass index, temperature, time to complete a procedure.
  • Qualitative Data: Non-numerical, descriptive data.
    • Examples: Team colors, patient symptoms.
    • Levels of Measurement:
      • Nominal: Labels variables without quantitative value (e.g., gender).
      • Ordinal: Variables in naturally ordered categories (e.g., ranking, rating scale).

Variables

  • Independent Variable: Controlled variable (e.g., type of diet).
  • Dependent Variable: Measured or tested variable (e.g., blood sugar level, test scores).

Basic Statistical Terms

  • Parameters: Numerical descriptions of populations.
  • Statistics: Numerical descriptions of samples.
  • Parametric Statistics: Statistical methods used with normally distributed data, usually continuous.
  • Non-parametric Statistics: Statistical methods used with non-normally distributed data, especially categorical or ordinal.
  • Descriptive Statistics: Summarizing and describing data.
  • Inferential Statistics: Making inferences or predictions about populations based on samples.

Statistical Analysis Goes Beyond Data Analysis

  • Statistical analysis provides insights and guides decisions beyond just describing data.

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