AC

MMI-L2-Moodle

Lecture Overview

  • Course Title: MVLS: Life Sciences Level 2

  • Topic: Contemporary Issues in Biology 2 Block 2

  • Lecturers: Dr. Stevie A Bain (she/her), Dr. Paul Capewell (he/him)

Intended Learning Outcomes

  • Difference Between Samples and Populations: Understanding the distinction between individual samples and entire populations.

  • Sampling Error: Definition and explanation of how sampling error can occur.

  • Data Classification: Identify and present data as continuous or categorical appropriately.

  • Statistical Hypotheses: Formulate null hypotheses (H0) and alternative hypotheses (H1) for various experiments.

  • Chi-squared Test: Overview of the chi-squared test, including calculations (observed counts, expected counts, degrees of freedom, p-values). Note: R code not required for memorization.

Data Presentation

Class Height Data

  • Visualization: Histogram for MMI1$Height with various heights marked.

Histograms

  • Hist() Command Modifiers:

    • Number of bins: breaks

    • X-axis label: xlab

    • Y-axis label: ylab

    • Bar color: col

    • Main title: main

    • X-axis limit: xlim

    • Y-axis limit: ylim

Samples and Populations

  • Definition of Sample: A sample is a very small subset of a total population.

  • Population: All members constituting a defined group.

  • Representativeness: Questions of how well a sample represents the population and potential sampling biases.

Investigation Approach

  • Random Subsets: Conduct random sampling to perform statistical analyses (mean, median, range).

  • Impact on Results: Explore whether random sampling influences outcomes.

Sampling Error

  • Definition: Random variation in data arising from sampling only part of the population.

Factors Influencing Human Height

  • Various influences include:

    • Genetic factors (inheritance, parental height)

    • Environmental conditions (nutrition, lifestyle)

    • Societal factors (socioeconomic status, beauty standards)

    • Biological factors (hormone levels, medical conditions)

    • Geographical location and climate influences

Biological Sex as a Modifier of Height

  • Importance of collecting data on biological sex alongside height data.

  • Participation in data collection is voluntary and anonymous.

Data Analysis Steps

  • Clean data using Excel.

  • Use R Studio for visualization and statistical analysis.

  • Acknowledge differences between continuous and categorical data in presentation and analysis.

Expected Outcomes

  • Discuss inferences regarding expected sex ratios based on genetic determination.

  • Comparison of expected versus observed graphs will be assessed.

Importance of Statistical Testing

  • Mere observation of data is insufficient; statistical testing validates findings.

  • Consider if deviations from expectations result from sampling error or other factors.

Hypotheses in Statistics

  • Statistical Hypothesis: An assumption about population characteristics or variable relationships subject to testing.

  • Null Hypothesis (H0):

    • Default assumption that there’s no relationship or effect.

    • Categorical outcomes assumed equally likely.

  • Alternative Hypothesis (H1):

    • Suggests a difference exists; categorical outcomes are not equally likely.

Sex Ratio Contextual Hypotheses

  • Design hypotheses to evaluate sex ratios using:

    • H0: Male to female ratio = 1:1

    • H1: Male to female ratio ≠ 1:1

    • Further variations can be hypothesized (more females or males).

Chi-squared Test

  • Application: Used for categorical data to compare observed versus expected frequencies under H0.

  • Assumptions:

    • Categories are mutually exclusive.

    • Observations are independent.

    • Expected values mostly > 5.

  • Formula:

    • ( \chi^2 = \sum \frac{(d^2)}{e} ) where ( d ) = observed - expected.

Contingency Table and Degrees of Freedom

  • Construct a contingency table to display observed counts.

  • Calculate degrees of freedom using: (rows - 1) x (columns - 1).

Calculating p-values

  • Example p-value chart provided for significance levels based on the chi-squared statistic and degrees of freedom.

Using R for Analysis

  • Utilization of R for expedited statistical analysis and p-value computations.

Upcoming Topics

  • Tomorrow's Lecture: Focus on interpreting p-values and assess the role of genetic sex as a modifier of human height.