1) What are inferential statistics?

2) What is hypothesis testing?

3. What is a null and an alternative hypothesis?

4. How do we talk about null and alternative hypotheses?

5. What is alpha?

6. What is the p value and what does it mean to be statistically significant?

7. What does it mean for a result to be inferred to the population?

8. What are some limitations of hypothesis testing?

9. Which types of question can be answered by hypothesis testing and which cannot?

Answer:

1. What are inferential statistics?

An inferential statistic is  a statistical method that focus on making predictions or generalizations about a larger data set, based on a sample of those data. Examples of inferential statistics include:

• One sample test of difference/One sample hypothesis test.
• Confidence Interval.
• Contingency Tables and Chi Square Statistic.
• T-test or ANOVA.
• Pearson Correlation.
• Bi-variate Regression.
• Multi-variate Regression

2. What is hypothesis testing?

Hypothesis testing is a form of statistical inference that uses data from a sample to draw conclusions about a population parameter. This study results to the rejection or retention of the null hypothesis, which is a tentative assumption made about a population parameter.

3. What is a null and an alternative hypothesis?

Null hypothesis is a type of hypothesis which assumes that there is NO significant difference between certain population characteristics.

Alternative hypothesis is a type of hypothesis  in which a difference (or an effect) between between certain population characteristics is anticipated by the researchers. It is the opposite of the null hypothesis.

4. How do we talk about null and alternative hypotheses?

Null and alternative hypotheses are two mutually exclusive statements. About null hypothesis, one would speak of an assumption that there is no association between two groups. While, An alternative hypothesis is a statement that describes that there is a relationship between two selected variables in a study.

5. What is alpha?

Alpha level is the probability of rejecting the null hypothesis when the null hypothesis is true (Probability of type I error in a study). It's always chosen by the researcher based on their study.

6. What is the p value and what does it mean to be statistically significant?

P value (calculated probability) is the estimated probability of rejecting the null hypothesis of a study question when that hypothesis is true. (*Take p-value as an evidence against null hypothesis)

A result is said to be statistically significant if the p-value is less than alpha. We reject the null hypothesis for this case. This means that the the result is not likely to be caused by chance for a given statistical significance level.

7. What does it mean for a result to be inferred to the population?

To infer from a population means to generalize the results about properties of the population or the underlying probability distribution of the population from a sample survey.

8. What are some limitations of hypothesis testing?

• Hypothesis testing simply indicate whether the difference is due to fluctuations of sampling or because of other reasons but the tests do not tell us reasons why the differences exists.
• There is high chance of erring for small sample sizes. This results to misleading outcomes.
• Results of significance tests are based on probabilities and as such cannot be expressed with full certainty. 

9. Which types of question can be answered by hypothesis testing and which cannot?

Hypothesis testing is used to answer al scientific questions which are experimental or testable, for example, tossing a fair die.

Hypothesis testing, however, cannot be used to test non-scientific questions (Pseudo science questions). You cannot proof the non-existence of anything. For example Hypotheses that cannot be tested, for cause and effect attributed to a supernatural being or an invisible fifth dimension that cannot be detected.