Aly Moosa

Kanya Shah

🤔Drawing conclusions from the data and analyzing possible areas of error helps create a valid inference about the population from which the sample was chosen.

You can conclude that changes in the explanatory variable cause changes in the response variable if the results of an experiment are **statistically significant**. That occurs when the observed results of a study/experiment are too unusual to be explained by chance alone, so the results are called statistically significant. We will learn more about how to determine if differences are enough to be considered statistically significant in Unit 6 and Unit 7.

If the experimental units were chosen from a large population, then the results can be generalized to that population or larger group. Random selection of units ensures that the data will be representative of the designated population 👨👩👧👦.

***Inference about a population **can be made only if the individuals from a population taking part in the study were randomly selected.

*A well designed experiment that randomly assigns experimental units to treatments allows **inferences about cause and effect**.

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Big Reviews: Finals & Exam Prep

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Free Response Questions (FRQs)

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Unit 1: Exploring One-Variable Data

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Unit 2: Exploring Two-Variable Data

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Unit 3: Collecting Data

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Unit 4: Probability, Random Variables, and Probability Distributions

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Unit 5: Sampling Distributions

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Unit 6: Inference for Categorical Data: Proportions

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Unit 7: Inference for Qualitative Data: Means

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Unit 8: Inference for Categorical Data: Chi-Square

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Unit 9: Inference for Quantitative Data: Slopes

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