Podcast
Questions and Answers
Independent variables are also known as ______ variables.
Independent variables are also known as ______ variables.
predictor
In hypothesis testing, a ______ hypothesis is a statement that there is no effect or no difference.
In hypothesis testing, a ______ hypothesis is a statement that there is no effect or no difference.
null
The mean, median, and variance are all examples of ______ statistics.
The mean, median, and variance are all examples of ______ statistics.
descriptive
A significant ______ value indicates that the results observed are unlikely due to random chance.
A significant ______ value indicates that the results observed are unlikely due to random chance.
Signup and view all the answers
Type I and Type II errors relate to mistakes made when testing a ______ hypothesis.
Type I and Type II errors relate to mistakes made when testing a ______ hypothesis.
Signup and view all the answers
Data distributions can often be described as ______ or t-distributions.
Data distributions can often be described as ______ or t-distributions.
Signup and view all the answers
The concepts of ______ and precision are critical in assessing measurement accuracy.
The concepts of ______ and precision are critical in assessing measurement accuracy.
Signup and view all the answers
Reproducibility in experiments means obtaining the same results under ______ conditions.
Reproducibility in experiments means obtaining the same results under ______ conditions.
Signup and view all the answers
Study Notes
Data Analysis and Biostatistics
-
Variables: Characteristics observed in subjects or objects.
- Independent (predictor): Variables believed to cause change.
- Dependent (response): Variables affected by the independent variable.
-
Data Types:
-
Categorical: Qualitative data (binary, nominal, ordinal).
- Binary: Two categories (e.g., yes/no).
- Nominal: Categories with no inherent order (e.g., colors).
- Ordinal: Categories with a meaningful order (e.g., pain levels).
-
Continuous: Quantitative data that can take on any value within a range (e.g., height).
- Discrete: Continuous variables that can only take on specific values (e.g., counting animals).
-
Categorical: Qualitative data (binary, nominal, ordinal).
-
Descriptive Statistics: Summarizing and describing data.
- Central Tendency: Measures like mean, median, and mode.
- Dispersion (Spread): Measures like variance and standard deviation.
-
Probability and Distributions:
- Normal Distribution (Gaussian Distribution): A bell-shaped distribution.
- t Distribution: Used for small sample sizes when the population standard deviation is unknown.
-
Hypothesis Testing: Determining if there's a significant difference between groups.
- Null Hypothesis (H0): There is no effect or difference.
- Alternative Hypothesis (Ha): There is an effect or difference.
- Standard Error: Measures the variability of a sample mean.
- P-value: Probability of observing the results if the null hypothesis is true.
- Confidence Intervals: Range of values that likely contains the true population parameter.
- Effect Size: The magnitude of an effect or difference.
-
Type I and Type II Errors:
- Type I Error: Rejecting the null hypothesis when it's true. (False positive)
- Type II Error: Failing to reject the null hypothesis when it's false. (False negative)
- Box Plots: Visual representation of data distribution, showing median, quartiles, and outliers.
- Falsification Principle: Easier to disprove a hypothesis than to prove it.
Statistical Analysis
- Regression Analysis: Examining the relationship between variables.
- Correlation: A measure of the linear association between two variables.
- Linear Regression: Fitting a straight line to data points.
- Multiple Regression: Examining the relationship between multiple independent variables and a single dependent variable.
Accuracy and Precision
- Accuracy: Closeness of a measurement to the true value.
- Precision: Reproducibility of a measurement.
- Systematic Error (Bias): Consistent deviation from the true value.
- Random Error: Variation due to chance.
- Invariable Errors: Errors that reduce accuracy due to incorrect instrumentation used, etc.
Statistical Significance
- P-Values: Probability of observing results as extreme or more extreme if the null hypothesis is true.
-
Statistical Significance: When the p-value is below a predetermined threshold (often 0.05).
- Statistical Significance ≠ Practical Significance: A statistically significant result might not be meaningful in the real world.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Related Documents
Description
Test your understanding of key concepts in data analysis and biostatistics. This quiz covers variables, data types, descriptive statistics, and probability distributions. Evaluate your knowledge and readiness to apply these fundamental principles in real-world scenarios.
