Scientific Experiments & Data Interpretation

• Understanding scientific experiments and interpreting data is a valuable skill applicable across various professions.

Scientific Experiments and the Scientific Method

• Scientific experiments start with observations that lead to a hypothesis, which offers a testable explanation for a phenomenon.
• Experiments are designed to test the hypothesis's validity, and the data gathered is presented in charts, graphs, or scientific writing.
• The ability to interpret graphs and charts is crucial in determining whether to accept a hypothesis, which may lead to new hypotheses and tests.
• The basic steps of a scientific experiment include:
• Making observations
• Formulating a hypothesis
• Designing and conducting an experiment
• Collecting and evaluating data
• Accepting or rejecting the hypothesis, and if necessary
• Formulating a new hypothesis

Creating & Reading Graphs

• Independent variables (x-variable) are the cause being manipulated.
• Dependent variables (y-variable) are the effect influenced by the independent variable.
• Numerical data is in the form of numbers.
• Categorical data is non-numeric.
• The type of data available affects the choice of the appropriate graph or chart.

Types of data

• Numerical data: numbers
• Categorical data: non-numerical values

Line Graphs and Tables

• Tables list x and y values next to each other.
• Line graphs chart these values as data points (x, y).
• Line graphs compare numerical data and create a visual of trends.
• The x-axis is horizontal and is used to plot all of the independent x values.
• The y-axis is vertical and is used to plot all of the dependent y values.

Interpreting Graphs & Charts

• Different types of data require different graphs and charts.
• Models include line graphs, bar charts, pie charts, and flow charts.

Line Graphs

• Line graphs plot independent (x) and dependent (y) variables to create a visual trend.
• The x-axis is the horizontal axis
• The y-axis is the vertical axis
• Variables are represented as coordinate pairs, written as (x, y).
• Line graphs compare strictly numerical data and small changes to a trend over time.
• For example, a study tracks the number of days each summer for five years where temperatures reach 100 degrees or more.
• In 1990, there were 2 days.
• In 1991, there were 4.
• In 1992, there were 6.
• In 1993, there were 8.
• In 1994, there were 10.
• The independent variables (x values) are the years (1990, 1991, 1992, 1993, and 1994).
• The dependent variables (y values) are the number of hot days (2, 4, 6, 8, and 10).
• Coordinate pairs are (1990,2), (1991,4), (1992,6), (1993,8), and (1994,10).
• Coordinate pairs are plotted as single data points on the graph connected by a line to show the trend.
• Line graphs provide a visual representation of data trends, aiding predictions.

Bar Charts

• Bar charts use rectangular bars to represent values.
• The height of the bar is proportional to the value of the data it represents.
• Bar charts can be oriented vertically or horizontally.
• Bar charts are useful for comparing larger changes or differences in data among different groups and can be used with non-numerical data.
• For example, a bar chart displaying the average age of citizens in different American cities.
• Each bar stands for a different city (x)
• The height of the bar indicates average age (y).
• If the chart indicates that the average age in Chicago is greater than in New York, the average ages might be 50 and 40, respectively.

Pie Charts

• Pie charts are useful when comparing percentages.
• Each slice represents a percentage of a whole data set.
• Slices represent independent variables (x), while the slice size indicates the dependent variable (y).
• For example, a pie chart represents US trade deficit by country
• The entire graph represents the total US trade deficit
• Each slice represents a different country
• The slice size corresponds to the deficit amount
• The largest slice is China representing $343 billion dollars and 47.2% of the total deficit.

Flow Charts

• Flow charts compare non-numerical concepts.
• Flow charts are diagrams showing relationships between concepts or steps in a process, simplifying complex relationships.
• Flow charts display steps in sequential order and often show cause-and-effect relationships in science, despite not providing numerical data.
• For example, a flowchart of the ACTH negative feedback loop effectively displays sequential interactions between body systems.
• The flowchart begins at the hypothalamus.
• Low cortisol levels trigger the hypothalamus to release CRH, stimulating the pituitary gland.
• The pituitary gland releases ACTH into the bloodstream.
• High ACTH levels trigger the adrenal cortex to produce cortisol.
• Cortisol in the blood slows CRH and ACTH production, reducing cortisol production.
• When cortisol levels drop low enough, the cycle restarts.

Learn the basics of scientific experiments, including forming a hypothesis and designing the experiment. Discover how to collect and evaluate data. Understand how to read graphs, interpret data, and determine whether to accept a hypothesis.