Praxis General Science (5436)

Experimental investigations involve manipulating variables under controlled conditions to establish cause-and-effect relationships. Laboratory experiments offer maximum control but may sacrifice real-world applicability. Field experiments take place in natural settings, offering greater ecological validity but less control over variables.

Observational studies gather data without manipulating variables. These studies can reveal correlations and patterns but cannot establish causation. Longitudinal studies follow subjects over extended periods, while cross-sectional studies examine subjects at a single point in time. Case studies provide in-depth analysis of individual subjects or events.

Comparative investigations examine similarities and differences among groups, organisms, or phenomena. These studies often generate hypotheses for further experimental testing. Modeling investigations use physical, mathematical, or computer models to represent and study complex systems that are difficult to observe or manipulate directly.

Well-designed experiments allow scientists to test hypotheses by manipulating variables under controlled conditions. The independent variable is the factor the experimenter deliberately changes or manipulates. The dependent variable is the factor measured or observed in response to changes in the independent variable. Controlled variables, or constants, are factors held the same throughout the experiment to ensure that observed effects result from the independent variable alone.

A control group serves as a baseline for comparison. It receives no treatment or a placebo treatment, allowing researchers to determine whether observed changes result from the experimental treatment rather than other factors. The experimental group receives the treatment being tested.

Sample size refers to the number of subjects or trials in an experiment. Larger sample sizes generally produce more reliable results by reducing the impact of random variation. Replication involves repeating experiments to verify that results are consistent and reproducible. Results that cannot be replicated by independent researchers are considered unreliable.

Randomization involves assigning subjects to groups randomly to prevent bias and ensure that groups are comparable. In double-blind experiments, neither the subjects nor the researchers collecting data know which group receives the treatment, eliminating both subject expectations and researcher bias from affecting results.

Proper data recording ensures accuracy and allows replication. Laboratory notebooks provide permanent records of procedures, observations, and results. Entries should be made in ink, dated, and never erased. Mistakes should be crossed out with a single line so the original entry remains legible.

Data tables organize information systematically. Tables should include descriptive titles, clearly labeled columns with units, and sufficient trials for statistical analysis. Raw data should be recorded before any calculations or processing.

Data analysis transforms raw observations into meaningful information. Quantitative data require statistical analysis to identify patterns, relationships, and significance. Measures of central tendency include mean (arithmetic average), median (middle value), and mode (most frequent value). Measures of variability include range, variance, and standard deviation, which describe how spread out data points are from the central value.

Graphs and charts present data visually, making patterns easier to identify. Line graphs show changes over time or relationships between continuous variables. Bar graphs compare discrete categories. Scatter plots reveal relationships between two continuous variables. Pie charts show proportions of a whole.

Correlation describes the relationship between two variables. Positive correlation means both variables increase together. Negative correlation means one variable increases as the other decreases. Correlation coefficients range from −1 to +1, with values closer to these extremes indicating stronger relationships. However, correlation does not prove causation. Two variables may correlate because of a third confounding variable or pure coincidence.

Statistical significance indicates whether observed results likely reflect a real effect rather than random chance. Scientists typically use a significance level of 0.05, meaning there is less than a 5% probability that results occurred by chance. P-values below this threshold suggest the null hypothesis (that no effect exists) can be rejected.

Graphs convey relationships visually. The independent variable conventionally appears on the x-axis, and the dependent variable on the y-axis. Axes should be labeled clearly with variable names and units. Scales should be appropriate for the data range and allow easy reading of values.

Line graphs connect data points to show trends and changes. They are appropriate when the independent variable is continuous. Best-fit lines or curves may be drawn to represent the overall relationship, even if not all points fall exactly on the line.

Bar graphs compare discrete categories. Bars should be separated by spaces, and categories should be labeled clearly. Double bar graphs can compare multiple data sets across the same categories.

Scatter plots show relationships between two continuous variables without connecting points. The pattern of points reveals correlation: positive, negative, or none. Trend lines can be added to quantify the relationship.

Histograms display frequency distributions of continuous data grouped into intervals. Unlike bar graphs, histogram bars touch because they represent continuous data ranges.