| Alternative hypothesis |
What will happen according to the hypothesis, the opposite of the null hypothesis. e.g. 'Students who sleep more perform better on tests.' |
| Averages |
Central values of data. e.g. mean, median, mode - measures of central tendency |
| Between participants |
Dividing participants into groups for each condition of the experiment. e.g. Group A receives a drug, Group B receives a placebo. |
| Bimodal |
If there are two modes, and they're clearly higher (peeks) than other values. Unimodal (1 mode) and multimodal (multiple modes) |
| Categorical & ranking data |
Non-numeric data grouped by category or order. e.g. blood type (categorical), race position (ranking). |
| Confounding variables |
Uncontrolled variables that affect the outcome. e.g. diet affecting weight loss in an exercise study. |
| Correlational co-efficient |
Strength and direction of variable relationship. e.g. Pearson's r = 0.85 indicates strong positive correlation. week13 |
| Dependent variable |
The measured outcome, e.g. heart rate change |
| Descriptive statistics |
Descriptions of the data from the sample. e.g. a graph or table of data, mean or median. |
| Distribution |
How data values are spread. e.g. normal, skewed, or bimodal. |
| Experimental design |
A research design involving manipulation of an independent variable. e.g. testing whether caffeine improves memory by assigning participants to caffeine or no caffeine groups. |
| Falsification |
Concept that a hypothesis must be disprovable. e.g. 'All swans are white' can be falsified by finding one black swan. |
| Frequency distribution |
Counts of values in intervals. e.g. histogram of age groups. |
| Generalizing |
Generalising from the sample to the population |
| Global Samples |
Diverse samples from various locations. e.g. participants from multiple countries. |
| Graph types |
Scatter plot, histogram, bar chart. |
| Hypothesis testing |
Statistical procedure to determine support for a hypothesis. e.g. testing if a new drug reduces symptoms more than a placebo. |
| Hypothesis: one tailed |
A hypothesis that predicts the direction of the two variables, e.g. extra cheese leads to worst sleep |
| Hypothesis: two tailed |
A hypothesis that is neutral about the direction or relationship of the two variables |
| Independent Variable |
The manipulated variable. e.g. film category watched |
| Inferential statistics |
Statistical tools that allow researchers to their sample to the population. |
| Line of best fit |
Line approximating data trend in a scatterplot. e.g. average trend in exam scores vs. study time. |
| Mean |
Sum of values divided by number. e.g. average test score. |
| Median |
Middle value in an ordered dataset. e.g. the 5th value in a list of 9 scores. |
| Measures of central tendency |
Mode, median, mean |
| Mode |
The most common result, e.g. scores 1,3, 5, 6, 6, 7 it would be 6. There would be no mode if they're all unique. |
| NHST |
TODO |
| Normal distribution |
The mean, median, and mode are equal. The data is symmetric (bell curve). The data tapers off gradually. AKA Gaussian distribution |
| P-Value |
Probability between 0 and 1 |
| Qualitative data |
Descriptive data. e.g. interviews. |
| Quantitative data |
Numerical data. e.g. height, temperature. |
| Quasi experimental |
An experimental design without random assignment. e.g. comparing student performance across two existing classes. |
| Range |
Difference between highest and lowest values. e.g. if scores range from 60 to 90, the range is 30. |
| Sample |
Selecting a subset of a population. e.g. choosing 100 students from a university. |
| Sampling variation |
Each time you choose 10 subjects for your sample, you might get variation in the mean of the value you want to measure, e.g. height. It might also be different from the population. Sample size is important, and inferential statistics are used to generalise to the population. |
| Sampling error |
Error from using a sample instead of the full population. e.g. survey results differ from actual population values. |
| Sampling error: Type 1 |
Rejecting a true null hypothesis. e.g. concluding a drug works when it doesn't. |
| Sampling error: Type 2 |
Failing to reject a false null hypothesis. e.g. missing the effect of a useful drug. |
| Scale |
Measurement system classifying data. e.g. nominal, ordinal, interval, ratio scales. |
| Scatterplot |
Graph showing a two variable relationship. e.g. height vs. weight. |
| Standard deviation |
Spread of data around the mean. e.g. SD = 2 means most scores lie within 2 points of the mean. |
| Statistical significance |
In psychology, statistical significance occurs when the p-value, calculated from a test like chi-squared, is less than 0.05 (5%). This means there's less than a 5% chance the results are due to random variation if the null hypothesis is true, so we reject it for the alternative hypothesis. |
| Within participants |
The same group of people are affected by different conditions of the independent variable, e.g. juror's guilty verdict before and after deliberation |