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An alternative hypothesis directly contradicts the null hypothesis.

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Bias is the tendency of a statistic to overestimate or underestimate a parameter. Bias can be introduced due to sampling or measurement errors, or unrepresentative samples.

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A confounding variable is an “extra” variable that you didn’t account for. They can ruin an experiment and give you useless results.

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A dot plot is a graphical display of data using dots. Dot plots are used to display quantitative data.

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In those cases, the explanatory variable is used to predict or explain differences in the response variable. In an experimental study, the explanatory variable is the variable that is manipulated by the researcher.

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A summary consists of five values: the most extreme values in the data set (the maximum and minimum values), the lower and upper quartiles, and the median.

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A geometric setting occurs when we perform independent trials of the same chance process and record the number of trials until we receive a success.

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A histogram is a graphical display of data using bars of different heights. In a histogram, each bar groups numbers into ranges. Taller bars show that more data falls in that range. A histogram displays the shape and spread of continuous sample data.

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The interquartile range (IQR) is a measure of variability, based on dividing a data set into quartiles. It contains the middle 50% of the dataset. It is calculated by subtracting the first quartile from the third quartile.

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The probability of two events occurring together, for example, A and B.

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The law of large numbers says that when we observe more and more repetitions of a chance process, the proportion of times that a specific outcome occurs approaches a single value.

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A margin of error tells you how many percentage points your results will differ from the real population value. For example, a 95% confidence interval with a 4 percent margin of error means that your statistic will be within 4 percent of the real population value 95% of the time.

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A normal distribution is described by a normal density curve. It is a symmetric distribution where most of the observations cluster around the central peak and the probabilities for values further away from the mean taper off equally in both directions.  The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena.

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An observational study is a study where researchers observe the effect of a risk factor, diagnostic test, treatment or other intervention without trying to change who is or isn't exposed to it.

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The power of a test against an alternative is the probability that the test will reject the null hypothesis at a given significance level when the alternative is true.

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A quantitative variable is  a variable that is represented by numerical values.

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A residual is the difference between an observed value of the response variable and a predicted value from the regression line.
residual = observed y - predicted y

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A simple random sample(SRS) is a sample taken of n individuals from the population chosen in a way that every individual has an equal chance of being selected.

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A test statistic is a measurement of  how for a sample statistic varies from the null hypothesis if it were true, in standardized units.

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Undercoverage occurs when some groups in the population are left out of the sampling process. This is an example of a sampling error.

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Variability is how much the data set vary from the average value. The most common measures of variability are the range, the interquartile range (IQR), variance, and standard deviation.

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A weighted average is a way to calculate mean when some numbers in the data set carry more importance (weight) than others.

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X-bar is a symbol that represents the mean, specifically the sample mean. To find the value of x̄, you must divide the sum of the observations by the sample size (n).

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The y-intercept is part of the equation for a regression line(ŷ=a+bx). The y-intercept is the predicted value when x=0.

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A z-score, also known as a standard score, this value will tell you how far away a data point is from the mean. To calculate it, you need the population mean and population standard deviation. Z-scores can range from -3 standard deviations  to 3 standard deviations.